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  • What's the best way to move cars along roads

    - by David Thielen
    I am implementing car movement game (sort-of like Locomotion). So 60 times a second I have to advance the movement of each car. The problem is I have to look ahead to see if there is a slower car, stop sign, or red light ahead. And then slow down appropiately. I also want to have the cars take time to go from stopped to full speed and again to slow down. I'm not implementing full-blown physics, but just a tick by tick speed up/slow down as that provides most of the realism to match what people expect to see. The best I've come up with is to walk out the full distance the car would travel of it was slowing to a stop and see if anywhere along that path it needed to slow down or stop. And then move it forward appropiately. I am moving the cars 60 times a second so I need this to be fast. And walking out that whole path each tick strikes me as processor intensive. What's the best way to do this?

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  • XNA 2D Top Down game - FOREACH didn't work for checking Enemy and Switch-Tile

    - by aldroid16
    Here is the gameplay. There is three condition. The player step on a Switch-Tile and it became false. 1) When the Enemy step on it (trapped) AND the player step on it too, the Enemy will be destroyed. 2) But when the Enemy step on it AND the player DIDN'T step on it too, the Enemy will be escaped. 3) If the Switch-Tile condition is true then nothing happened. The effect is activated when the Switch tile is false (player step on the Switch-Tile). Because there are a lot of Enemy and a lot of Switch-Tile, I have to use foreach loop. The problem is after the Enemy is ESCAPED (case 2) and step on another Switch-Tile again, nothing happened to the enemy! I didn't know what's wrong. The effect should be the same, but the Enemy pass the Switch tile like nothing happened (They should be trapped) Can someone tell me what's wrong? Here is the code : public static void switchUpdate(GameTime gameTime) { foreach (SwitchTile switch in switchTiles) { foreach (Enemy enemy in EnemyManager.Enemies) { if (switch.Active == false) { if (!enemy.Destroyed) { if (switch.IsCircleColliding(enemy.EnemyBase.WorldCenter, enemy.EnemyBase.CollisionRadius)) { enemy.EnemySpeed = 10; //reducing Enemy Speed if it enemy is step on the Tile (for about two seconds) enemy.Trapped = true; float elapsed = (float)gameTime.ElapsedGameTime.Milliseconds; moveCounter += elapsed; if (moveCounter> minMoveTime) { //After two seconds, if the player didn't step on Switch-Tile. //The Enemy escaped and its speed back to normal enemy.EnemySpeed = 60f; enemy.Trapped = false; } } } } else if (switch.Active == true && enemy.Trapped == true && switch.IsCircleColliding(enemy.EnemyBase.WorldCenter, enemy.EnemyBase.CollisionRadius) ) { //When the Player step on Switch-Tile and //there is an enemy too on this tile which was trapped = Destroy Enemy enemy.Destroyed = true; } } } }

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  • Realistic Jumping

    - by Seth Taddiken
    I want to make the jumping that my character does more realistic. This is what I've tried so far but it doesn't seem very realistic when the player jumps. I want it to jump up at a certain speed then slow down as it gets to the top then eventually stopping (for about one frame) and then slowly going back down but going faster and faster as it goes back down. I've been trying to make the speed at which the player jumps up slow down by one each frame then become negative and go down faster... but it doesn't work very well public bool isPlayerDown = true; public bool maxJumpLimit = false; public bool gravityReality = false; public bool leftWall = false; public bool rightWall = false; public float x = 76f; public float y = 405f; if (Keyboard.GetState().IsKeyDown(up) && this.isPlayerDown == true && this.y <= 405f) { this.isPlayerDown = false; } if (this.isPlayerDown == false && this.maxJumpLimit == false) { this.y = this.y - 6; } if (this.y <= 200) { this.maxJumpLimit = true; } if (this.isPlayerDown == true) { this.y = 405f; this.isPlayerDown = true; this.maxJumpLimit = false; } if (this.gravityReality == true) { this.y = this.y + 2f; this.gravityReality = false; } if (this.maxJumpLimit == true) { this.y = this.y + 2f; this.gravityReality = true; } if (this.y > 405f) { this.isPlayerDown = true; }

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  • Animating sprites in HTML5 canvas

    - by fnx
    I'm creating a 2D platformer game with HTML5 canvas and javascript. I'm having a bit of a struggle with animations. Currently I animate by getting preloaded images from an array, and the code is really simple, in player.update() I call a function that does this: var animLength = this.animations[id].length; this.counter++; this.counter %= 3; if (this.counter == 2) this.spriteCounter++; this.spriteCounter %= animLength; return this.animations[id][this.spriteCounter]; There are a couple of problems with this one: When the player does 2 actions that require animating at the same time, animation speed doubles. Apparently this.counter++ is working twice at the same time. I imagine that if I start animating multiple sprites with this, the animation speed will multiply by the amount of sprites. Other issue is that I couldn't make the animation run only once instead of looping while key is held down. Someone told me that I should create a function Animation(animation id, isLooped boolean) and the use something like player.sprite = new Animation("explode", false) but I don't know how to make it work. Yes I'm a noob... :)

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  • Whats a good host for an active vBulletin site?

    - by Kyle
    I've been switching hosts using a VPS each time and I'm just really not sure I'm finding the right VPS's. I've used a VPS from burst.net & rubyringtech and I just feel like it's slowly killing my site because of the slow speed. I really don't know if it's the network or the VPS itself but I really wish to fix this. When I TOP into the VPS peak times it shows this: top - 03:18:56 up 16:33, 1 user, load average: 1.33, 1.40, 1.33 Tasks: 30 total, 1 running, 29 sleeping, 0 stopped, 0 zombie Cpu(s): 27.2%us, 13.6%sy, 0.0%ni, 59.2%id, 0.0%wa, 0.0%hi, 0.0%si, 0.0%st Mem: 1048576k total, 679712k used, 368864k free, 0k buffers Swap: 0k total, 0k used, 0k free, 0k cached And pages take atleast a good 2-3 minutes to load. I have only like 50-60 members on the forum also. I had a shared hosting account and the forum was lightning fast.... Is a VPS a bad idea? :\ What should I do to fix this? I'm running lighttpd with xcache, and the latest mysql + php version. The server is a intel i7 2600 w/ 1gb uplink (I think the 1gb uplink is a lie because I've tested the network and the highest download speed I've seen was 20mb/s from a code.google page) All in all I've seen people talking about linode. Should I try them? I honestly don't need a dedicated server yet it's only 50-70 members online. What should I do? I really want a VPS because I enjoy root access. Does anyone have any suggestions?

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  • How can I make smoother upwards/downwards controls in pygame?

    - by Zolani13
    This is a loop I use to interpret key events in a python game. # Event Loop for event in pygame.event.get(): if event.type == QUIT: pygame.quit() sys.exit() if event.type == pygame.KEYDOWN: if event.key == pygame.K_a: my_speed = -10; if event.key == pygame.K_d: my_speed = 10; if event.type == pygame.KEYUP: if event.key == pygame.K_a: my_speed = 0; if event.key == pygame.K_d: my_speed = 0; The 'A' key represents up, while the 'D' key represents down. I use this loop within a larger drawing loop, that moves the sprite using this: Paddle1.rect.y += my_speed; I'm just making a simple pong game (as my first real code/non-gamemaker game) but there's a problem between moving upwards <= downwards. Essentially, if I hold a button upwards (or downwards), and then press downwards (or upwards), now holding both buttons, the direction will change, which is a good thing. But if I then release the upward button, then the sprite will stop. It won't continue in the direction of my second input. This kind of key pressing is actually common with WASD users, when changing directions quickly. Few people remember to let go of the first button before pressing the second. But my program doesn't accommodate the habit. I think I understand the reason, which is that when I let go of my first key, the KEYUP event still triggers, setting the speed to 0. I need to make sure that if a key is released, it only sets the speed to 0 if another key isn't being pressed. But the interpreter will only go through one event at a time, I think, so I can't check if a key has been pressed if it's only interpreting the commands for a released key. This is my dilemma. I want set the key controls so that a player doesn't have to press one button at a time to move upwards <= downwards, making it smoother. How can I do that?

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  • TechEd 2012: Fast SQL Server

    - by Tim Murphy
    While I spend a certain amount of my time creating databases (coding around SQL Server and setup a server when I have to) it isn’t my bread and butter.  Since I have run into a number of time that SQL Server needed to be tuned I figured I would step out of my comfort zone and see what I can learn. Brent Ozar packed a mountain of information into his session on making SQL Server faster.  I’m not sure how he found time to hit all of his points since he was allowing the audience abuse him on Twitter instead of asking questions, but he managed it.  I also questioned his sanity since he appeared to be using a fruit laptop. He had my attention though when he stated that he had given up on telling people to not use “select *”. He posited that it could be fixed with hardware by caching the data in memory.  He continued by cautioning that having too many indexes could defeat this approach.  His logic was sound if not always practical, but it was a good place to start when determining the trade-offs you need to balance.  He was moving pretty fast, but I believe he was prescribing this solution predominately for OLTP database prior to moving on to data warehouse solutions. Much of the advice he gave for data warehouses is contained in the Microsoft Fast Track guidance so I won’t rehash it here.  To summarize the solution seems to be the proper balance memory, disk access speed and the speed of the pipes that get the data from storage to the CPU.  It appears to be sound guidance and the session gave enough information that going forward we should be able to find the details needed easily.  Just what the doctor ordered. del.icio.us Tags: SQL Server,TechEd,TechEd 2012,Database,Performance Tuning

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  • Why is my USB data transfer so slow?

    - by Dave M G
    Whenever I do any kind of file transfer using USB, whether to a USB stick, or with my Android phone, or anything else, it is ridiculously slow. It says 59.8 KB/sec, which would be an awesome speed if this were 1991 and I was using a modem to dial up to my local BBS. Surely USB technology is better than that...? 37 seconds to move less data than the equivelent of 1 MP3 file? Also, regardless of what it says about speed and time, the reality is much, much slower. I routinely see it say something like "37 seconds left" and have to wait for minutes. Sometimes, if I want to move large amounts of files, it can say it will take 8 hours or more. Is this normal? My computer may not be the most awesome on the market, and about a year old, but it's an i5 with 4GB RAM and modern components, so surely this isn't the hardware's fault. What can I do to get better USB data transfer performance? Also, I did look at this question, but my newbie eyes don't see anything that look like an actual solution, just a lot of discussion about what transfer rates could or should be. Update: As requested in the comments, I've generated a whole bunch of output from the command line, and put it on Ubuntu Pastebin. Please see it here.

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  • Predictive firing (in a tile-based game)

    - by n00bster
    I have a (turn-based) tile-based game, in which you can shoot at entities. You can move around with mouse and keyboard, it's all tile-based, except that bullets move "freely". I've got it all working just fine except that when I move, and the creatures shoot towards the player, they shoot towards the previous tiles.. resulting in ugly looking "miss hits" or lag. I think I need to implement some kind of predictive firing based on the bullet speed and the distance, but I don't quite know how to implement such a thing... Here's a simplified snip of my firing code. class Weapon { public void fire(int x, int y) { ... ... ... Creature owner = getOwner(); Tile targetTile = Zone.getTileAt(x, y); float dist = Vector.distance(owner.getCenterPosition(), targetTile.getCenterPosition()); Bullet b = new Bullet(); b.setPosition(owner.getCenterPosition()); // Take dist into account in the duration to get constant speed regardless of distance float duration = dist / 600f; // Moves the bullet to the centre of the target tile in the given amount of time (in seconds) b.moveTo(targetTile.getCenterPosition(), duration); // This is what I'm after // Vector v = predict the position // b.moveTo(v, duration); Zone.add(bullet); // Now the bullet gets "ticked" and moveTo will be implemented } } Movement of creatures is as simple as setting the position variable. If you need more information, just ask.

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  • what's wrong with my lookAt and move forward code?

    - by alaslipknot
    so am still in the process of getting familiar with libGdx and one of the fun things i love to do is to make basics method for reusability on future projects, and for now am stacked on getting a Sprite rotate toward target (vector2) and then move forward based on that rotation the code am using is this : // set angle public void lookAt(Vector2 target) { float angle = (float) Math.atan2(target.y - this.position.y, target.x - this.position.x); angle = (float) (angle * (180 / Math.PI)); setAngle(angle); } // move forward public void moveForward() { this.position.x += Math.cos(getAngle())*this.speed; this.position.y += Math.sin(getAngle())*this.speed; } and this is my render method : @Override public void render(float delta) { // TODO Auto-generated method stub Gdx.gl.glClearColor(0, 0, 0.0f, 1); Gdx.gl.glClear(GL20.GL_COLOR_BUFFER_BIT); // groupUpdate(); Vector3 mousePos = new Vector3(Gdx.input.getX(), Gdx.input.getY(), 0); camera.unproject(mousePos); ball.lookAt(new Vector2(mousePos.x, mousePos.y)); // if (Gdx.input.isTouched()) { ball.moveForward(); } batch.begin(); batch.draw(ball.getSprite(), ball.getPos().x, ball.getPos().y, ball .getSprite().getOriginX(), ball.getSprite().getOriginY(), ball .getSprite().getWidth(), ball.getSprite().getHeight(), .5f, .5f, ball.getAngle()); batch.end(); } the goal is to make the ball always look at the mouse cursor, and then move forward when i click, am also using this camera : // create the camera and the SpriteBatch camera = new OrthographicCamera(); camera.setToOrtho(false, 800, 480); aaaand the result was so creepy lol Thank you

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  • Monday at Oracle OpenWorld 2012 - Must See Session: “Using the Right Tools, Techniques, and Technologies for Integration Projects”

    - by Lionel Dubreuil
    Don’t miss this “CON8669 - Using the Right Tools, Techniques, and Technologies for Integration Projects“ session with Timothy Hall - Sr. Director, Oracle: Date: Monday, Oct 1, Time: 3:15 PM - 4:15 PM Location: Moscone South - 308 Every integration project brings its own unique set of challenges. There are many tools and techniques to choose from. How do you ensure that you have a means of consistently and repeatedly making decisions about which tools, techniques, and technologies are used? In working with many customers around the globe, Oracle has developed a set of criteria to help evaluate a variety of common integration questions. This session explores these criteria and how they have been further organized into decision trees that offer a repeatable means for ensuring that project teams are given the same guidance from project to project. Using these techniques, the presentation shows how you can reduce risk and speed productivity for your projects Objectives for this session are to: Discuss common questions that arise at the start of integration projects Review various decision criteria and approaches for getting to a consistent set of answers Explore how these techniques can be used to reduce risk and speed productivity Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif";}

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  • Scaling sprite velocity / co-ordinatesin Android

    - by user22241
    I'm trying to find the answer to a question that I've had for a long time, but am having trouble finding it! I hope someone can help :-) I'm trying to find information on how to scale sprite velocity / movement / co-ordinates. What I mean by this is how do I get a sprite to move at the same speed relative to the screen size / DPI so that it takes the same amount of real-time to get from one side of the screen to the other? All of the posts pertaining to sprite scaling that I can find on the various forums relate to the size of the sprite, but this part of it I'm OK with so far, it's just that when I move a sprite, it kind of gets there at different speed depending on the dpi / resolution of the device. I hope I'm making sense. This is the code I have so far, instead of using explicit amounts, like 1, I'm using something like the following: platSpeedFloat= (1 * (dpi/160)); //Use '1' so on an MDPI screen, the sprite will move by 1 physical pixel Then basically what I'm doing is something like this: (all varialble previously declared) platSpeedSave+=platSpeedFloat; //Add the platSpeedFloat value to the current platSpeedSave value platSpeed=(int) platSpeedSave; //Cast to int so it can be checked in the following statement if (platSpeed==platSpeedSave) //Check the casted int value to float value stored previoiusly {floorY=floorY-platSpeed; //If they match then change the Y value platSpeedSave=0;} //Reset Would be grateful if someone could assists - hope I'm making sense. The above doesn't seems to work the sprite moves 'faster' on lower DPI screens. Thanks

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  • colliding btRigidBody objects behave strangely when moving slowly

    - by Piku
    I'm trying to use Bullet Physics in my iOS game. The engine appears to be correctly compiled in that the demos work fine. In my game I have the player's ship and some enemy ships. They're defined as btRigidBody objects and btCollisionObjects and I'm using btSphereShapes for collision. At 'fast' speeds, collisions appear to happen sensibly - things collide and nothing goes 'weird'. If the speeds are very slow though and the player's ship touches a non-moving object the collision happens, but then the player's ship moves at incredible speed over the next few frames and appears a long distance from where it collided - completely out of proportion to the speed it was moving before impact. To move the things around I'm using setLinearVelocity() each frame, ticking the physics engine, then using getMotionState() to update the rendering code I have. Part of the issue might be I don't quite understand how to set the correct mass or what the best speeds are to use for anything. I'm mostly sticking numbers in and seeing what happens. Should I be using Bullet in this way, and are there any guidelines for deciding on the mass of objects? (am I right in assuming that in collisions heavier objects will force lighter objects to move more)

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  • Memcached Lagging

    - by Brad Dwyer
    Let me preface this by saying that this is a followup question to this topic. That was "solved" by switching from Solaris (SmartOS) to Ubuntu for the memcached server. Now we've multiplied load by about 5x and are running into problems again. We are running a site that is doing about 1000 requests/minute, each request hits Memcached with approximately 3 reads and 1 write. So load is approximately 65 requests per second. Total data in the cache is about 37M, and each key contains a very small amount of data (a JSON-encoded array of integers amounting to less than 1K). We have setup a benchmarking script on these pages and fed the data into StatsD for logging. The problem is that there are spikes where Memcached takes a very long time to respond. These do not appear to correlate with spikes in traffic. What could be causing these spikes? Why would memcached take over a second to reply? We just booted up a second server to put in the pool and it didn't make any noticeable difference in the frequency or severity of the spikes. This is the output of getStats() on the servers: Array ( [-----------] => Array ( [pid] => 1364 [uptime] => 3715684 [threads] => 4 [time] => 1336596719 [pointer_size] => 64 [rusage_user_seconds] => 7924 [rusage_user_microseconds] => 170000 [rusage_system_seconds] => 187214 [rusage_system_microseconds] => 190000 [curr_items] => 12578 [total_items] => 53516300 [limit_maxbytes] => 943718400 [curr_connections] => 14 [total_connections] => 72550117 [connection_structures] => 165 [bytes] => 2616068 [cmd_get] => 450388258 [cmd_set] => 53493365 [get_hits] => 450388258 [get_misses] => 2244297 [evictions] => 0 [bytes_read] => 2138744916 [bytes_written] => 745275216 [version] => 1.4.2 ) [-----------:11211] => Array ( [pid] => 8099 [uptime] => 4687 [threads] => 4 [time] => 1336596719 [pointer_size] => 64 [rusage_user_seconds] => 7 [rusage_user_microseconds] => 170000 [rusage_system_seconds] => 290 [rusage_system_microseconds] => 990000 [curr_items] => 2384 [total_items] => 225964 [limit_maxbytes] => 943718400 [curr_connections] => 7 [total_connections] => 588097 [connection_structures] => 91 [bytes] => 562641 [cmd_get] => 1012562 [cmd_set] => 225778 [get_hits] => 1012562 [get_misses] => 125161 [evictions] => 0 [bytes_read] => 91270698 [bytes_written] => 350071516 [version] => 1.4.2 ) ) Edit: Here is the result of a set and retrieve of 10,000 values. Normal: Stored 10000 values in 5.6118 seconds. Average: 0.0006 High: 0.1958 Low: 0.0003 Fetched 10000 values in 5.1215 seconds. Average: 0.0005 High: 0.0141 Low: 0.0003 When Spiking: Stored 10000 values in 16.5074 seconds. Average: 0.0017 High: 0.9288 Low: 0.0003 Fetched 10000 values in 19.8771 seconds. Average: 0.0020 High: 0.9478 Low: 0.0003

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  • CLSF & CLK 2013 Trip Report by Jeff Liu

    - by jamesmorris
    This is a contributed post from Jeff Liu, lead XFS developer for the Oracle mainline Linux kernel team. Recently, I attended both the China Linux Storage and Filesystem workshop (CLSF), and the China Linux Kernel conference (CLK), which were held in Shanghai. Here are the highlights for both events. CLSF - 17th October XFS update (led by Jeff Liu) XFS keeps rapid progress with a lot of changes, especially focused on the infrastructure/performance improvements as well as  new feature development.  This can be reflected with a sample statistics among XFS/Ext4+JBD2/Btrfs via: # git diff --stat --minimal -C -M v3.7..v3.12-rc4 -- fs/xfs|fs/ext4+fs/jbd2|fs/btrfs XFS: 141 files changed, 27598 insertions(+), 19113 deletions(-) Ext4+JBD2: 39 files changed, 10487 insertions(+), 5454 deletions(-) Btrfs: 70 files changed, 19875 insertions(+), 8130 deletions(-) What made up those changes in XFS? Self-describing metadata(CRC32c). This is a new feature and it contributed about 70% code changes, it can be enabled via `mkfs.xfs -m crc=1 /dev/xxx` for v5 superblock. Transaction log space reservation improvements. With this change, we can calculate the log space reservation at mount time rather than runtime to reduce the the CPU overhead. User namespace support. So both XFS and USERNS can be enabled on kernel configuration begin from Linux 3.10. Thanks Dwight Engen's efforts for this thing. Split project/group quota inodes. Originally, project quota can not be enabled with group quota at the same time because they were share the same quota file inode, now it works but only for v5 super block. i.e, CRC enabled. CONFIG_XFS_WARN, an new lightweight runtime debugger which can be deployed in production environment. Readahead log object recovery, this change can speed up the log replay progress significantly. Speculative preallocation inode tracking, clearing and throttling. The main purpose is to deal with inodes with post-EOF space due to speculative preallocation, support improved quota management to free up a significant amount of unwritten space when at or near EDQUOT. It support backgroup scanning which occurs on a longish interval(5 mins by default, tunable), and on-demand scanning/trimming via ioctl(2). Bitter arguments ensued from this session, especially for the comparison between Ext4 and Btrfs in different areas, I have to spent a whole morning of the 1st day answering those questions. We basically agreed on XFS is the best choice in Linux nowadays because: Stable, XFS has a good record in stability in the past 10 years. Fengguang Wu who lead the 0-day kernel test project also said that he has observed less error than other filesystems in the past 1+ years, I own it to the XFS upstream code reviewer, they always performing serious code review as well as testing. Good performance for large/small files, XFS does not works very well for small files has already been an old story for years. Best choice (maybe) for distributed PB filesystems. e.g, Ceph recommends delopy OSD daemon on XFS because Ext4 has limited xattr size. Best choice for large storage (>16TB). Ext4 does not support a single file more than around 15.95TB. Scalability, any objection to XFS is best in this point? :) XFS is better to deal with transaction concurrency than Ext4, why? The maximum size of the log in XFS is 2038MB compare to 128MB in Ext4. Misc. Ext4 is widely used and it has been proved fast/stable in various loads and scenarios, XFS just need more customers, and Btrfs is still on the road to be a manhood. Ceph Introduction (Led by Li Wang) This a hot topic.  Li gave us a nice introduction about the design as well as their current works. Actually, Ceph client has been included in Linux kernel since 2.6.34 and supported by Openstack since Folsom but it seems that it has not yet been widely deployment in production environment. Their major work is focus on the inline data support to separate the metadata and data storage, reduce the file access time, i.e, a file access need communication twice, fetch the metadata from MDS and then get data from OSD, and also, the small file access is limited by the network latency. The solution is, for the small files they would like to store the data at metadata so that when accessing a small file, the metadata server can push both metadata and data to the client at the same time. In this way, they can reduce the overhead of calculating the data offset and save the communication to OSD. For this feature, they have only run some small scale testing but really saw noticeable improvements. Test environment: Intel 2 CPU 12 Core, 64GB RAM, Ubuntu 12.04, Ceph 0.56.6 with 200GB SATA disk, 15 OSD, 1 MDS, 1 MON. The sequence read performance for 1K size files improved about 50%. I have asked Li and Zheng Yan (the core developer of Ceph, who also worked on Btrfs) whether Ceph is really stable and can be deployed at production environment for large scale PB level storage, but they can not give a positive answer, looks Ceph even does not spread over Dreamhost (subject to confirmation). From Li, they only deployed Ceph for a small scale storage(32 nodes) although they'd like to try 6000 nodes in the future. Improve Linux swap for Flash storage (led by Shaohua Li) Because of high density, low power and low price, flash storage (SSD) is a good candidate to partially replace DRAM. A quick answer for this is using SSD as swap. But Linux swap is designed for slow hard disk storage, so there are a lot of challenges to efficiently use SSD for swap. SWAPOUT swap_map scan swap_map is the in-memory data structure to track swap disk usage, but it is a slow linear scan. It will become a bottleneck while finding many adjacent pages in the use of SSD. Shaohua Li have changed it to a cluster(128K) list, resulting in O(1) algorithm. However, this apporoach needs restrictive cluster alignment and only enabled for SSD. IO pattern In most cases, the swap io is in interleaved pattern because of mutiple reclaimers or a free cluster is shared by all reclaimers. Even though block layer can merge interleaved IO to some extent, but we cannot count on it completely. Hence the per-cpu cluster is added base on the previous change, it can help reclaimer do sequential IO and the block layer will be easier to merge IO. TLB flush: If we're reclaiming one active page, we should first move the page from active lru list to inactive lru list, and then reclaim the page from inactive lru to swap it out. During the process, we need to clear PTE twice: first is 'A'(ACCESS) bit, second is 'P'(PRESENT) bit. Processors need to send lots of ipi which make the TLB flush really expensive. Some works have been done to improve this, including rework smp_call_functiom_many() or remove the first TLB flush in x86, but there still have some arguments here and only parts of works have been pushed to mainline. SWAPIN: Page fault does iodepth=1 sync io, but it's a little waste if only issue a page size's IO. The obvious solution is doing swap readahead. But the current in-kernel swap readahead is arbitary(always 8 pages), and it always doesn't perform well for both random and sequential access workload. Shaohua introduced a new flag for madvise(MADV_WILLNEED) to do swap prefetch, so the changes happen in userspace API and leave the in-kernel readahead unchanged(but I think some improvement can also be done here). SWAP discard As we know, discard is important for SSD write throughout, but the current swap discard implementation is synchronous. He changed it to async discard which allow discard and write run in the same time. Meanwhile, the unit of discard is also optimized to cluster. Misc: lock contention For many concurrent swapout and swapin , the lock contention such as anon_vma or swap_lock is high, so he changed the swap_lock to a per-swap lock. But there still have some lock contention in very high speed SSD because of swapcache address_space lock. Zproject (led by Bob Liu) Bob gave us a very nice introduction about the current memory compression status. Now there are 3 projects(zswap/zram/zcache) which all aim at smooth swap IO storm and promote performance, but they all have their own pros and cons. ZSWAP It is implemented based on frontswap API and it uses a dynamic allocater named Zbud to allocate free pages. Zbud means pairs of zpages are "buddied" and it can only store at most two compressed pages in one page frame, so the max compress ratio is 50%. Each page frame is lru-linked and can do shink in memory pressure. If the compressed memory pool reach its limitation, shink or reclaim happens. It decompress the page frame into two new allocated pages and then write them to real swap device, but it can fail when allocating the two pages. ZRAM Acts as a compressed ramdisk and used as swap device, and it use zsmalloc as its allocator which has high density but may have fragmentation issues. Besides, page reclaim is hard since it will need more pages to uncompress and free just one page. ZRAM is preferred by embedded system which may not have any real swap device. Now both ZRAM and ZSWAP are in driver/staging tree, and in the mm community there are some disscussions of merging ZRAM into ZSWAP or viceversa, but no agreement yet. ZCACHE Handles file page compression but it is removed out of staging recently. From industry (led by Tang Jie, LSI) An LSI engineer introduced several new produces to us. The first is raid5/6 cards that it use full stripe writes to improve performance. The 2nd one he introduced is SandForce flash controller, who can understand data file types (data entropy) to reduce write amplification (WA) for nearly all writes. It's called DuraWrite and typical WA is 0.5. What's more, if enable its Dynamic Logical Capacity function module, the controller can do data compression which is transparent to upper layer. LSI testing shows that with this virtual capacity enables 1x TB drive can support up to 2x TB capacity, but the application must monitor free flash space to maintain optimal performance and to guard against free flash space exhaustion. He said the most useful application is for datebase. Another thing I think it's worth to mention is that a NV-DRAM memory in NMR/Raptor which is directly exposed to host system. Applications can directly access the NV-DRAM via a memory address - using standard system call mmap(). He said that it is very useful for database logging now. This kind of NVM produces are beginning to appear in recent years, and it is said that Samsung is building a research center in China for related produces. IMHO, NVM will bring an effect to current os layer especially on file system, e.g. its journaling may need to redesign to fully utilize these nonvolatile memory. OCFS2 (led by Canquan Shen) Without a doubt, HuaWei is the biggest contributor to OCFS2 in the past two years. They have posted 46 upstream patches and 39 patches have been merged. Their current project is based on 32/64 nodes cluster, but they also tried 128 nodes at the experimental stage. The major work they are working is to support ATS (atomic test and set), it can be works with DLM at the same time. Looks this idea is inspired by the vmware VMFS locking, i.e, http://blogs.vmware.com/vsphere/2012/05/vmfs-locking-uncovered.html CLK - 18th October 2013 Improving Linux Development with Better Tools (Andi Kleen) This talk focused on how to find/solve bugs along with the Linux complexity growing. Generally, we can do this with the following kind of tools: Static code checkers tools. e.g, sparse, smatch, coccinelle, clang checker, checkpatch, gcc -W/LTO, stanse. This can help check a lot of things, simple mistakes, complex problems, but the challenges are: some are very slow, false positives, may need a concentrated effort to get false positives down. Especially, no static checker I found can follow indirect calls (“OO in C”, common in kernel): struct foo_ops { int (*do_foo)(struct foo *obj); } foo->do_foo(foo); Dynamic runtime checkers, e.g, thread checkers, kmemcheck, lockdep. Ideally all kernel code would come with a test suite, then someone could run all the dynamic checkers. Fuzzers/test suites. e.g, Trinity is a great tool, it finds many bugs, but needs manual model for each syscall. Modern fuzzers around using automatic feedback, but notfor kernel yet: http://taviso.decsystem.org/making_software_dumber.pdf Debuggers/Tracers to understand code, e.g, ftrace, can dump on events/oops/custom triggers, but still too much overhead in many cases to run always during debug. Tools to read/understand source, e.g, grep/cscope work great for many cases, but do not understand indirect pointers (OO in C model used in kernel), give us all “do_foo” instances: struct foo_ops { int (*do_foo)(struct foo *obj); } = { .do_foo = my_foo }; foo>do_foo(foo); That would be great to have a cscope like tool that understands this based on types/initializers XFS: The High Performance Enterprise File System (Jeff Liu) [slides] I gave a talk for introducing the disk layout, unique features, as well as the recent changes.   The slides include some charts to reflect the performances between XFS/Btrfs/Ext4 for small files. About a dozen users raised their hands when I asking who has experienced with XFS. I remembered that when I asked the same question in LinuxCon/Japan, only 3 people raised their hands, but they are Chris Mason, Ric Wheeler, and another attendee. The attendee questions were mainly focused on stability, and comparison with other file systems. Linux Containers (Feng Gao) The speaker introduced us that the purpose for those kind of namespaces, include mount/UTS/IPC/Network/Pid/User, as well as the system API/ABI. For the userspace tools, He mainly focus on the Libvirt LXC rather than us(LXC). Libvirt LXC is another userspace container management tool, implemented as one type of libvirt driver, it can manage containers, create namespace, create private filesystem layout for container, Create devices for container and setup resources controller via cgroup. In this talk, Feng also mentioned another two possible new namespaces in the future, the 1st is the audit, but not sure if it should be assigned to user namespace or not. Another is about syslog, but the question is do we really need it? In-memory Compression (Bob Liu) Same as CLSF, a nice introduction that I have already mentioned above. Misc There were some other talks related to ACPI based memory hotplug, smart wake-affinity in scheduler etc., but my head is not big enough to record all those things. -- Jeff Liu

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  • Metro: Understanding CSS Media Queries

    - by Stephen.Walther
    If you are building a Metro style application then your application needs to look great when used on a wide variety of devices. Your application needs to work on tiny little phones, slates, desktop monitors, and the super high resolution displays of the future. Your application also must support portable devices used with different orientations. If someone tilts their phone from portrait to landscape mode then your application must still be usable. Finally, your Metro style application must look great in different states. For example, your Metro application can be in a “snapped state” when it is shrunk so it can share screen real estate with another application. In this blog post, you learn how to use Cascading Style Sheet media queries to support different devices, different device orientations, and different application states. First, you are provided with an overview of the W3C Media Query recommendation and you learn how to detect standard media features. Next, you learn about the Microsoft extensions to media queries which are supported in Metro style applications. For example, you learn how to use the –ms-view-state feature to detect whether an application is in a “snapped state” or “fill state”. Finally, you learn how to programmatically detect the features of a device and the state of an application. You learn how to use the msMatchMedia() method to execute a media query with JavaScript. Using CSS Media Queries Media queries enable you to apply different styles depending on the features of a device. Media queries are not only supported by Metro style applications, most modern web browsers now support media queries including Google Chrome 4+, Mozilla Firefox 3.5+, Apple Safari 4+, and Microsoft Internet Explorer 9+. Loading Different Style Sheets with Media Queries Imagine, for example, that you want to display different content depending on the horizontal resolution of a device. In that case, you can load different style sheets optimized for different sized devices. Consider the following HTML page: <!DOCTYPE html> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <title>U.S. Robotics and Mechanical Men</title> <link href="main.css" rel="stylesheet" type="text/css" /> <!-- Less than 1100px --> <link href="medium.css" rel="stylesheet" type="text/css" media="(max-width:1100px)" /> <!-- Less than 800px --> <link href="small.css" rel="stylesheet" type="text/css" media="(max-width:800px)" /> </head> <body> <div id="header"> <h1>U.S. Robotics and Mechanical Men</h1> </div> <!-- Advertisement Column --> <div id="leftColumn"> <img src="advertisement1.gif" alt="advertisement" /> <img src="advertisement2.jpg" alt="advertisement" /> </div> <!-- Product Search Form --> <div id="mainContentColumn"> <label>Search Products</label> <input id="search" /><button>Search</button> </div> <!-- Deal of the Day Column --> <div id="rightColumn"> <h1>Deal of the Day!</h1> <p> Buy two cameras and get a third camera for free! Offer is good for today only. </p> </div> </body> </html> The HTML page above contains three columns: a leftColumn, mainContentColumn, and rightColumn. When the page is displayed on a low resolution device, such as a phone, only the mainContentColumn appears: When the page is displayed in a medium resolution device, such as a slate, both the leftColumn and the mainContentColumns are displayed: Finally, when the page is displayed in a high-resolution device, such as a computer monitor, all three columns are displayed: Different content is displayed with the help of media queries. The page above contains three style sheet links. Two of the style links include a media attribute: <link href="main.css" rel="stylesheet" type="text/css" /> <!-- Less than 1100px --> <link href="medium.css" rel="stylesheet" type="text/css" media="(max-width:1100px)" /> <!-- Less than 800px --> <link href="small.css" rel="stylesheet" type="text/css" media="(max-width:800px)" /> The main.css style sheet contains default styles for the elements in the page. The medium.css style sheet is applied when the page width is less than 1100px. This style sheet hides the rightColumn and changes the page background color to lime: html { background-color: lime; } #rightColumn { display:none; } Finally, the small.css style sheet is loaded when the page width is less than 800px. This style sheet hides the leftColumn and changes the page background color to red: html { background-color: red; } #leftColumn { display:none; } The different style sheets are applied as you stretch and contract your browser window. You don’t need to refresh the page after changing the size of the page for a media query to be applied: Using the @media Rule You don’t need to divide your styles into separate files to take advantage of media queries. You can group styles by using the @media rule. For example, the following HTML page contains one set of styles which are applied when a device’s orientation is portrait and another set of styles when a device’s orientation is landscape: <!DOCTYPE html> <html> <head> <meta charset="utf-8" /> <title>Application1</title> <style type="text/css"> html { font-family:'Segoe UI Semilight'; font-size: xx-large; } @media screen and (orientation:landscape) { html { background-color: lime; } p.content { width: 50%; margin: auto; } } @media screen and (orientation:portrait) { html { background-color: red; } p.content { width: 90%; margin: auto; } } </style> </head> <body> <p class="content"> Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Maecenas porttitor congue massa. Fusce posuere, magna sed pulvinar ultricies, purus lectus malesuada libero, sit amet commodo magna eros quis urna. </p> </body> </html> When a device has a landscape orientation then the background color is set to the color lime and the text only takes up 50% of the available horizontal space: When the device has a portrait orientation then the background color is red and the text takes up 90% of the available horizontal space: Using Standard CSS Media Features The official list of standard media features is contained in the W3C CSS Media Query recommendation located here: http://www.w3.org/TR/css3-mediaqueries/ Here is the official list of the 13 media features described in the standard: · width – The current width of the viewport · height – The current height of the viewport · device-width – The width of the device · device-height – The height of the device · orientation – The value portrait or landscape · aspect-ratio – The ratio of width to height · device-aspect-ratio – The ratio of device width to device height · color – The number of bits per color supported by the device · color-index – The number of colors in the color lookup table of the device · monochrome – The number of bits in the monochrome frame buffer · resolution – The density of the pixels supported by the device · scan – The values progressive or interlace (used for TVs) · grid – The values 0 or 1 which indicate whether the device supports a grid or a bitmap Many of the media features in the list above support the min- and max- prefix. For example, you can test for the min-width using a query like this: (min-width:800px) You can use the logical and operator with media queries when you need to check whether a device supports more than one feature. For example, the following query returns true only when the width of the device is between 800 and 1,200 pixels: (min-width:800px) and (max-width:1200px) Finally, you can use the different media types – all, braille, embossed, handheld, print, projection, screen, speech, tty, tv — with a media query. For example, the following media query only applies to a page when a page is being printed in color: print and (color) If you don’t specify a media type then media type all is assumed. Using Metro Style Media Features Microsoft has extended the standard list of media features which you can include in a media query with two custom media features: · -ms-high-contrast – The values any, black-white, white-black · -ms-view-state – The values full-screen, fill, snapped, device-portrait You can take advantage of the –ms-high-contrast media feature to make your web application more accessible to individuals with disabilities. In high contrast mode, you should make your application easier to use for individuals with vision disabilities. The –ms-view-state media feature enables you to detect the state of an application. For example, when an application is snapped, the application only occupies part of the available screen real estate. The snapped application appears on the left or right side of the screen and the rest of the screen real estate is dominated by the fill application (Metro style applications can only be snapped on devices with a horizontal resolution of greater than 1,366 pixels). Here is a page which contains style rules for an application in both a snap and fill application state: <!DOCTYPE html> <html> <head> <meta charset="utf-8" /> <title>MyWinWebApp</title> <style type="text/css"> html { font-family:'Segoe UI Semilight'; font-size: xx-large; } @media screen and (-ms-view-state:snapped) { html { background-color: lime; } } @media screen and (-ms-view-state:fill) { html { background-color: red; } } </style> </head> <body> <p class="content"> Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Maecenas porttitor congue massa. Fusce posuere, magna sed pulvinar ultricies, purus lectus malesuada libero, sit amet commodo magna eros quis urna. </p> </body> </html> When the application is snapped, the application appears with a lime background color: When the application state is fill then the background color changes to red: When the application takes up the entire screen real estate – it is not in snapped or fill state – then no special style rules apply and the application appears with a white background color. Querying Media Features with JavaScript You can perform media queries using JavaScript by taking advantage of the window.msMatchMedia() method. This method returns a MSMediaQueryList which has a matches method that represents success or failure. For example, the following code checks whether the current device is in portrait mode: if (window.msMatchMedia("(orientation:portrait)").matches) { console.log("portrait"); } else { console.log("landscape"); } If the matches property returns true, then the device is in portrait mode and the message “portrait” is written to the Visual Studio JavaScript Console window. Otherwise, the message “landscape” is written to the JavaScript Console window. You can create an event listener which triggers code whenever the results of a media query changes. For example, the following code writes a message to the JavaScript Console whenever the current device is switched into or out of Portrait mode: window.msMatchMedia("(orientation:portrait)").addListener(function (mql) { if (mql.matches) { console.log("Switched to portrait"); } }); Be aware that the event listener is triggered whenever the result of the media query changes. So the event listener is triggered both when you switch from landscape to portrait and when you switch from portrait to landscape. For this reason, you need to verify that the matches property has the value true before writing the message. Summary The goal of this blog entry was to explain how CSS media queries work in the context of a Metro style application written with JavaScript. First, you were provided with an overview of the W3C CSS Media Query recommendation. You learned about the standard media features which you can query such as width and orientation. Next, we focused on the Microsoft extensions to media queries. You learned how to use –ms-view-state to detect whether a Metro style application is in “snapped” or “fill” state. You also learned how to use the msMatchMedia() method to perform a media query from JavaScript.

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  • SQL SERVER – Shrinking Database is Bad – Increases Fragmentation – Reduces Performance

    - by pinaldave
    Earlier, I had written two articles related to Shrinking Database. I wrote about why Shrinking Database is not good. SQL SERVER – SHRINKDATABASE For Every Database in the SQL Server SQL SERVER – What the Business Says Is Not What the Business Wants I received many comments on Why Database Shrinking is bad. Today we will go over a very interesting example that I have created for the same. Here are the quick steps of the example. Create a test database Create two tables and populate with data Check the size of both the tables Size of database is very low Check the Fragmentation of one table Fragmentation will be very low Truncate another table Check the size of the table Check the fragmentation of the one table Fragmentation will be very low SHRINK Database Check the size of the table Check the fragmentation of the one table Fragmentation will be very HIGH REBUILD index on one table Check the size of the table Size of database is very HIGH Check the fragmentation of the one table Fragmentation will be very low Here is the script for the same. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO Let us check the table size and fragmentation. Now let us TRUNCATE the table and check the size and Fragmentation. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can clearly see that after TRUNCATE, the size of the database is not reduced and it is still the same as before TRUNCATE operation. After the Shrinking database operation, we were able to reduce the size of the database. If you notice the fragmentation, it is considerably high. The major problem with the Shrink operation is that it increases fragmentation of the database to very high value. Higher fragmentation reduces the performance of the database as reading from that particular table becomes very expensive. One of the ways to reduce the fragmentation is to rebuild index on the database. Let us rebuild the index and observe fragmentation and database size. -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REBUILD GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can notice that after rebuilding, Fragmentation reduces to a very low value (almost same to original value); however the database size increases way higher than the original. Before rebuilding, the size of the database was 5 MB, and after rebuilding, it is around 20 MB. Regular rebuilding the index is rebuild in the same user database where the index is placed. This usually increases the size of the database. Look at irony of the Shrinking database. One person shrinks the database to gain space (thinking it will help performance), which leads to increase in fragmentation (reducing performance). To reduce the fragmentation, one rebuilds index, which leads to size of the database to increase way more than the original size of the database (before shrinking). Well, by Shrinking, one did not gain what he was looking for usually. Rebuild indexing is not the best suggestion as that will create database grow again. I have always remembered the excellent post from Paul Randal regarding Shrinking the database is bad. I suggest every one to read that for accuracy and interesting conversation. Let us run following script where we Shrink the database and REORGANIZE. -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Shrink the Database DBCC SHRINKDATABASE (ShrinkIsBed); GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REORGANIZE GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can see that REORGANIZE does not increase the size of the database or remove the fragmentation. Again, I no way suggest that REORGANIZE is the solution over here. This is purely observation using demo. Read the blog post of Paul Randal. Following script will clean up the database -- Clean up USE MASTER GO ALTER DATABASE ShrinkIsBed SET SINGLE_USER WITH ROLLBACK IMMEDIATE GO DROP DATABASE ShrinkIsBed GO There are few valid cases of the Shrinking database as well, but that is not covered in this blog post. We will cover that area some other time in future. Additionally, one can rebuild index in the tempdb as well, and we will also talk about the same in future. Brent has written a good summary blog post as well. Are you Shrinking your database? Well, when are you going to stop Shrinking it? Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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  • NoSQL Memcached API for MySQL: Latest Updates

    - by Mat Keep
    With data volumes exploding, it is vital to be able to ingest and query data at high speed. For this reason, MySQL has implemented NoSQL interfaces directly to the InnoDB and MySQL Cluster (NDB) storage engines, which bypass the SQL layer completely. Without SQL parsing and optimization, Key-Value data can be written directly to MySQL tables up to 9x faster, while maintaining ACID guarantees. In addition, users can continue to run complex queries with SQL across the same data set, providing real-time analytics to the business or anonymizing sensitive data before loading to big data platforms such as Hadoop, while still maintaining all of the advantages of their existing relational database infrastructure. This and more is discussed in the latest Guide to MySQL and NoSQL where you can learn more about using the APIs to scale new generations of web, cloud, mobile and social applications on the world's most widely deployed open source database The native Memcached API is part of the MySQL 5.6 Release Candidate, and is already available in the GA release of MySQL Cluster. By using the ubiquitous Memcached API for writing and reading data, developers can preserve their investments in Memcached infrastructure by re-using existing Memcached clients, while also eliminating the need for application changes. Speed, when combined with flexibility, is essential in the world of growing data volumes and variability. Complementing NoSQL access, support for on-line DDL (Data Definition Language) operations in MySQL 5.6 and MySQL Cluster enables DevOps teams to dynamically update their database schema to accommodate rapidly changing requirements, such as the need to capture additional data generated by their applications. These changes can be made without database downtime. Using the Memcached interface, developers do not need to define a schema at all when using MySQL Cluster. Lets look a little more closely at the Memcached implementations for both InnoDB and MySQL Cluster. Memcached Implementation for InnoDB The Memcached API for InnoDB is previewed as part of the MySQL 5.6 Release Candidate. As illustrated in the following figure, Memcached for InnoDB is implemented via a Memcached daemon plug-in to the mysqld process, with the Memcached protocol mapped to the native InnoDB API. Figure 1: Memcached API Implementation for InnoDB With the Memcached daemon running in the same process space, users get very low latency access to their data while also leveraging the scalability enhancements delivered with InnoDB and a simple deployment and management model. Multiple web / application servers can remotely access the Memcached / InnoDB server to get direct access to a shared data set. With simultaneous SQL access, users can maintain all the advanced functionality offered by InnoDB including support for Foreign Keys, XA transactions and complex JOIN operations. Benchmarks demonstrate that the NoSQL Memcached API for InnoDB delivers up to 9x higher performance than the SQL interface when inserting new key/value pairs, with a single low-end commodity server supporting nearly 70,000 Transactions per Second. Figure 2: Over 9x Faster INSERT Operations The delivered performance demonstrates MySQL with the native Memcached NoSQL interface is well suited for high-speed inserts with the added assurance of transactional guarantees. You can check out the latest Memcached / InnoDB developments and benchmarks here You can learn how to configure the Memcached API for InnoDB here Memcached Implementation for MySQL Cluster Memcached API support for MySQL Cluster was introduced with General Availability (GA) of the 7.2 release, and joins an extensive range of NoSQL interfaces that are already available for MySQL Cluster Like Memcached, MySQL Cluster provides a distributed hash table with in-memory performance. MySQL Cluster extends Memcached functionality by adding support for write-intensive workloads, a full relational model with ACID compliance (including persistence), rich query support, auto-sharding and 99.999% availability, with extensive management and monitoring capabilities. All writes are committed directly to MySQL Cluster, eliminating cache invalidation and the overhead of data consistency checking to ensure complete synchronization between the database and cache. Figure 3: Memcached API Implementation with MySQL Cluster Implementation is simple: 1. The application sends reads and writes to the Memcached process (using the standard Memcached API). 2. This invokes the Memcached Driver for NDB (which is part of the same process) 3. The NDB API is called, providing for very quick access to the data held in MySQL Cluster’s data nodes. The solution has been designed to be very flexible, allowing the application architect to find a configuration that best fits their needs. It is possible to co-locate the Memcached API in either the data nodes or application nodes, or alternatively within a dedicated Memcached layer. The benefit of this flexible approach to deployment is that users can configure behavior on a per-key-prefix basis (through tables in MySQL Cluster) and the application doesn’t have to care – it just uses the Memcached API and relies on the software to store data in the right place(s) and to keep everything synchronized. Using Memcached for Schema-less Data By default, every Key / Value is written to the same table with each Key / Value pair stored in a single row – thus allowing schema-less data storage. Alternatively, the developer can define a key-prefix so that each value is linked to a pre-defined column in a specific table. Of course if the application needs to access the same data through SQL then developers can map key prefixes to existing table columns, enabling Memcached access to schema-structured data already stored in MySQL Cluster. Conclusion Download the Guide to MySQL and NoSQL to learn more about NoSQL APIs and how you can use them to scale new generations of web, cloud, mobile and social applications on the world's most widely deployed open source database See how to build a social app with MySQL Cluster and the Memcached API from our on-demand webinar or take a look at the docs Don't hesitate to use the comments section below for any questions you may have 

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  • MySQL – Scalability on Amazon RDS: Scale out to multiple RDS instances

    - by Pinal Dave
    Today, I’d like to discuss getting better MySQL scalability on Amazon RDS. The question of the day: “What can you do when a MySQL database needs to scale write-intensive workloads beyond the capabilities of the largest available machine on Amazon RDS?” Let’s take a look. In a typical EC2/RDS set-up, users connect to app servers from their mobile devices and tablets, computers, browsers, etc.  Then app servers connect to an RDS instance (web/cloud services) and in some cases they might leverage some read-only replicas.   Figure 1. A typical RDS instance is a single-instance database, with read replicas.  This is not very good at handling high write-based throughput. As your application becomes more popular you can expect an increasing number of users, more transactions, and more accumulated data.  User interactions can become more challenging as the application adds more sophisticated capabilities. The result of all this positive activity: your MySQL database will inevitably begin to experience scalability pressures. What can you do? Broadly speaking, there are four options available to improve MySQL scalability on RDS. 1. Larger RDS Instances – If you’re not already using the maximum available RDS instance, you can always scale up – to larger hardware.  Bigger CPUs, more compute power, more memory et cetera. But the largest available RDS instance is still limited.  And they get expensive. “High-Memory Quadruple Extra Large DB Instance”: 68 GB of memory 26 ECUs (8 virtual cores with 3.25 ECUs each) 64-bit platform High I/O Capacity Provisioned IOPS Optimized: 1000Mbps 2. Provisioned IOPs – You can get provisioned IOPs and higher throughput on the I/O level. However, there is a hard limit with a maximum instance size and maximum number of provisioned IOPs you can buy from Amazon and you simply cannot scale beyond these hardware specifications. 3. Leverage Read Replicas – If your application permits, you can leverage read replicas to offload some reads from the master databases. But there are a limited number of replicas you can utilize and Amazon generally requires some modifications to your existing application. And read-replicas don’t help with write-intensive applications. 4. Multiple Database Instances – Amazon offers a fourth option: “You can implement partitioning,thereby spreading your data across multiple database Instances” (Link) However, Amazon does not offer any guidance or facilities to help you with this. “Multiple database instances” is not an RDS feature.  And Amazon doesn’t explain how to implement this idea. In fact, when asked, this is the response on an Amazon forum: Q: Is there any documents that describe the partition DB across multiple RDS? I need to use DB with more 1TB but exist a limitation during the create process, but I read in the any FAQ that you need to partition database, but I don’t find any documents that describe it. A: “DB partitioning/sharding is not an official feature of Amazon RDS or MySQL, but a technique to scale out database by using multiple database instances. The appropriate way to split data depends on the characteristics of the application or data set. Therefore, there is no concrete and specific guidance.” So now what? The answer is to scale out with ScaleBase. Amazon RDS with ScaleBase: What you get – MySQL Scalability! ScaleBase is specifically designed to scale out a single MySQL RDS instance into multiple MySQL instances. Critically, this is accomplished with no changes to your application code.  Your application continues to “see” one database.   ScaleBase does all the work of managing and enforcing an optimized data distribution policy to create multiple MySQL instances. With ScaleBase, data distribution, transactions, concurrency control, and two-phase commit are all 100% transparent and 100% ACID-compliant, so applications, services and tooling continue to interact with your distributed RDS as if it were a single MySQL instance. The result: now you can cost-effectively leverage multiple MySQL RDS instance to scale out write-intensive workloads to an unlimited number of users, transactions, and data. Amazon RDS with ScaleBase: What you keep – Everything! And how does this change your Amazon environment? 1. Keep your application, unchanged – There is no change your application development life-cycle at all.  You still use your existing development tools, frameworks and libraries.  Application quality assurance and testing cycles stay the same. And, critically, you stay with an ACID-compliant MySQL environment. 2. Keep your RDS value-added services – The value-added services that you rely on are all still available. Amazon will continue to handle database maintenance and updates for you. You can still leverage High Availability via Multi A-Z.  And, if it benefits youra application throughput, you can still use read replicas. 3. Keep your RDS administration – Finally the RDS monitoring and provisioning tools you rely on still work as they did before. With your one large MySQL instance, now split into multiple instances, you can actually use less expensive, smallersmaller available RDS hardware and continue to see better database performance. Conclusion Amazon RDS is a tremendous service, but it doesn’t offer solutions to scale beyond a single MySQL instance. Larger RDS instances get more expensive.  And when you max-out on the available hardware, you’re stuck.  Amazon recommends scaling out your single instance into multiple instances for transaction-intensive apps, but offers no services or guidance to help you. This is where ScaleBase comes in to save the day. It gives you a simple and effective way to create multiple MySQL RDS instances, while removing all the complexities typically caused by “DIY” sharding andwith no changes to your applications . With ScaleBase you continue to leverage the AWS/RDS ecosystem: commodity hardware and value added services like read replicas, multi A-Z, maintenance/updates and administration with monitoring tools and provisioning. SCALEBASE ON AMAZON If you’re curious to try ScaleBase on Amazon, it can be found here – Download NOW. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • General monitoring for SQL Server Analysis Services using Performance Monitor

    - by Testas
    A recent customer engagement required a setup of a monitoring solution for SSAS, due to the time restrictions placed upon this, native Windows Performance Monitor (Perfmon) and SQL Server Profiler Monitoring Tools was used as using a third party tool would have meant the customer providing an additional monitoring server that was not available.I wanted to outline the performance monitoring counters that was used to monitor the system on which SSAS was running. Due to the slow query performance that was occurring during certain scenarios, perfmon was used to establish if any pressure was being placed on the Disk, CPU or Memory subsystem when concurrent connections access the same query, and Profiler to pinpoint how the query was being managed within SSAS, profiler I will leave for another blogThis guide is not designed to provide a definitive list of what should be used when monitoring SSAS, different situations may require the addition or removal of counters as presented by the situation. However I hope that it serves as a good basis for starting your monitoring of SSAS. I would also like to acknowledge Chris Webb’s awesome chapters from “Expert Cube Development” that also helped shape my monitoring strategy:http://cwebbbi.spaces.live.com/blog/cns!7B84B0F2C239489A!6657.entrySimulating ConnectionsTo simulate the additional connections to the SSAS server whilst monitoring, I used ascmd to simulate multiple connections to the typical and worse performing queries that were identified by the customer. A similar sript can be downloaded from codeplex at http://www.codeplex.com/SQLSrvAnalysisSrvcs.     File name: ASCMD_StressTestingScripts.zip. Performance MonitorWithin performance monitor,  a counter log was created that contained the list of counters below. The important point to note when running the counter log is that the RUN AS property within the counter log properties should be changed to an account that has rights to the SSAS instance when monitoring MSAS counters. Failure to do so means that the counter log runs under the system account, no errors or warning are given while running the counter log, and it is not until you need to view the MSAS counters that they will not be displayed if run under the default account that has no right to SSAS. If your connection simulation takes hours, this could prove quite frustrating if not done beforehand JThe counters used……  Object Counter Instance Justification System Processor Queue legnth N/A Indicates how many threads are waiting for execution against the processor. If this counter is consistently higher than around 5 when processor utilization approaches 100%, then this is a good indication that there is more work (active threads) available (ready for execution) than the machine's processors are able to handle. System Context Switches/sec N/A Measures how frequently the processor has to switch from user- to kernel-mode to handle a request from a thread running in user mode. The heavier the workload running on your machine, the higher this counter will generally be, but over long term the value of this counter should remain fairly constant. If this counter suddenly starts increasing however, it may be an indicating of a malfunctioning device, especially if the Processor\Interrupts/sec\(_Total) counter on your machine shows a similar unexplained increase Process % Processor Time sqlservr Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process % Processor Time msmdsrv Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process Working Set sqlservr If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Process Working Set msmdsrv If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Processor % Processor Time _Total and individual cores measures the total utilization of your processor by all running processes. If multi-proc then be mindful only an average is provided Processor % Privileged Time _Total To see how the OS is handling basic IO requests. If kernel mode utilization is high, your machine is likely underpowered as it's too busy handling basic OS housekeeping functions to be able to effectively run other applications. Processor % User Time _Total To see how the applications is interacting from a processor perspective, a high percentage utilisation determine that the server is dealing with too many apps and may require increasing thje hardware or scaling out Processor Interrupts/sec _Total  The average rate, in incidents per second, at which the processor received and serviced hardware interrupts. Shoulr be consistant over time but a sudden unexplained increase could indicate a device malfunction which can be confirmed using the System\Context Switches/sec counter Memory Pages/sec N/A Indicates the rate at which pages are read from or written to disk to resolve hard page faults. This counter is a primary indicator of the kinds of faults that cause system-wide delays, this is the primary counter to watch for indication of possible insufficient RAM to meet your server's needs. A good idea here is to configure a perfmon alert that triggers when the number of pages per second exceeds 50 per paging disk on your system. May also want to see the configuration of the page file on the Server Memory Available Mbytes N/A is the amount of physical memory, in bytes, available to processes running on the computer. if this counter is greater than 10% of the actual RAM in your machine then you probably have more than enough RAM. monitor it regularly to see if any downward trend develops, and set an alert to trigger if it drops below 2% of the installed RAM. Physical Disk Disk Transfers/sec for each physical disk If it goes above 10 disk I/Os per second then you've got poor response time for your disk. Physical Disk Idle Time _total If Disk Transfers/sec is above  25 disk I/Os per second use this counter. which measures the percent time that your hard disk is idle during the measurement interval, and if you see this counter fall below 20% then you've likely got read/write requests queuing up for your disk which is unable to service these requests in a timely fashion. Physical Disk Disk queue legnth For the OLAP and SQL physical disk A value that is consistently less than 2 means that the disk system is handling the IO requests against the physical disk Network Interface Bytes Total/sec For the NIC Should be monitored over a period of time to see if there is anb increase/decrease in network utilisation Network Interface Current Bandwidth For the NIC is an estimate of the current bandwidth of the network interface in bits per second (BPS). MSAS 2005: Memory Memory Limit High KB N/A Shows (as a percentage) the high memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Limit Low KB N/A Shows (as a percentage) the low memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Usage KB N/A Displays the memory usage of the server process. MSAS 2005: Memory File Store KB N/A Displays the amount of memory that is reserved for the Cache. Note if total memory limit in the msmdsrv.ini is set to 0, no memory is reserved for the cache MSAS 2005: Storage Engine Query Queries from Cache Direct / sec N/A Displays the rate of queries answered from the cache directly MSAS 2005: Storage Engine Query Queries from Cache Filtered / Sec N/A Displays the Rate of queries answered by filtering existing cache entry. MSAS 2005: Storage Engine Query Queries from File / Sec N/A Displays the Rate of queries answered from files. MSAS 2005: Storage Engine Query Average time /query N/A Displays the average time of a query MSAS 2005: Connection Current connections N/A Displays the number of connections against the SSAS instance MSAS 2005: Connection Requests / sec N/A Displays the rate of query requests per second MSAS 2005: Locks Current Lock Waits N/A Displays thhe number of connections waiting on a lock MSAS 2005: Threads Query Pool job queue Length N/A The number of queries in the job queue MSAS 2005:Proc Aggregations Temp file bytes written/sec N/A Shows the number of bytes of data processed in a temporary file MSAS 2005:Proc Aggregations Temp file rows written/sec N/A Shows the number of bytes of data processed in a temporary file 

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  • How to Achieve OC4J RMI Load Balancing

    - by fip
    This is an old, Oracle SOA and OC4J 10G topic. In fact this is not even a SOA topic per se. Questions of RMI load balancing arise when you developed custom web applications accessing human tasks running off a remote SOA 10G cluster. Having returned from a customer who faced challenges with OC4J RMI load balancing, I felt there is still some confusions in the field how OC4J RMI load balancing work. Hence I decide to dust off an old tech note that I wrote a few years back and share it with the general public. Here is the tech note: Overview A typical use case in Oracle SOA is that you are building web based, custom human tasks UI that will interact with the task services housed in a remote BPEL 10G cluster. Or, in a more generic way, you are just building a web based application in Java that needs to interact with the EJBs in a remote OC4J cluster. In either case, you are talking to an OC4J cluster as RMI client. Then immediately you must ask yourself the following questions: 1. How do I make sure that the web application, as an RMI client, even distribute its load against all the nodes in the remote OC4J cluster? 2. How do I make sure that the web application, as an RMI client, is resilient to the node failures in the remote OC4J cluster, so that in the unlikely case when one of the remote OC4J nodes fail, my web application will continue to function? That is the topic of how to achieve load balancing with OC4J RMI client. Solutions You need to configure and code RMI load balancing in two places: 1. Provider URL can be specified with a comma separated list of URLs, so that the initial lookup will land to one of the available URLs. 2. Choose a proper value for the oracle.j2ee.rmi.loadBalance property, which, along side with the PROVIDER_URL property, is one of the JNDI properties passed to the JNDI lookup.(http://docs.oracle.com/cd/B31017_01/web.1013/b28958/rmi.htm#BABDGFBI) More details below: About the PROVIDER_URL The JNDI property java.name.provider.url's job is, when the client looks up for a new context at the very first time in the client session, to provide a list of RMI context The value of the JNDI property java.name.provider.url goes by the format of a single URL, or a comma separate list of URLs. A single URL. For example: opmn:ormi://host1:6003:oc4j_instance1/appName1 A comma separated list of multiple URLs. For examples:  opmn:ormi://host1:6003:oc4j_instanc1/appName, opmn:ormi://host2:6003:oc4j_instance1/appName, opmn:ormi://host3:6003:oc4j_instance1/appName When the client looks up for a new Context the very first time in the client session, it sends a query against the OPMN referenced by the provider URL. The OPMN host and port specifies the destination of such query, and the OC4J instance name and appName are actually the “where clause” of the query. When the PROVIDER URL reference a single OPMN server Let's consider the case when the provider url only reference a single OPMN server of the destination cluster. In this case, that single OPMN server receives the query and returns a list of the qualified Contexts from all OC4Js within the cluster, even though there is a single OPMN server in the provider URL. A context represent a particular starting point at a particular server for subsequent object lookup. For example, if the URL is opmn:ormi://host1:6003:oc4j_instance1/appName, then, OPMN will return the following contexts: appName on oc4j_instance1 on host1 appName on oc4j_instance1 on host2, appName on oc4j_instance1 on host3,  (provided that host1, host2, host3 are all in the same cluster) Please note that One OPMN will be sufficient to find the list of all contexts from the entire cluster that satisfy the JNDI lookup query. You can do an experiment by shutting down appName on host1, and observe that OPMN on host1 will still be able to return you appname on host2 and appName on host3. When the PROVIDER URL reference a comma separated list of multiple OPMN servers When the JNDI propery java.naming.provider.url references a comma separated list of multiple URLs, the lookup will return the exact same things as with the single OPMN server: a list of qualified Contexts from the cluster. The purpose of having multiple OPMN servers is to provide high availability in the initial context creation, such that if OPMN at host1 is unavailable, client will try the lookup via OPMN on host2, and so on. After the initial lookup returns and cache a list of contexts, the JNDI URL(s) are no longer used in the same client session. That explains why removing the 3rd URL from the list of JNDI URLs will not stop the client from getting the EJB on the 3rd server. About the oracle.j2ee.rmi.loadBalance Property After the client acquires the list of contexts, it will cache it at the client side as “list of available RMI contexts”.  This list includes all the servers in the destination cluster. This list will stay in the cache until the client session (JVM) ends. The RMI load balancing against the destination cluster is happening at the client side, as the client is switching between the members of the list. Whether and how often the client will fresh the Context from the list of Context is based on the value of the  oracle.j2ee.rmi.loadBalance. The documentation at http://docs.oracle.com/cd/B31017_01/web.1013/b28958/rmi.htm#BABDGFBI list all the available values for the oracle.j2ee.rmi.loadBalance. Value Description client If specified, the client interacts with the OC4J process that was initially chosen at the first lookup for the entire conversation. context Used for a Web client (servlet or JSP) that will access EJBs in a clustered OC4J environment. If specified, a new Context object for a randomly-selected OC4J instance will be returned each time InitialContext() is invoked. lookup Used for a standalone client that will access EJBs in a clustered OC4J environment. If specified, a new Context object for a randomly-selected OC4J instance will be created each time the client calls Context.lookup(). Please note the regardless of the setting of oracle.j2ee.rmi.loadBalance property, the “refresh” only occurs at the client. The client can only choose from the "list of available context" that was returned and cached from the very first lookup. That is, the client will merely get a new Context object from the “list of available RMI contexts” from the cache at the client side. The client will NOT go to the OPMN server again to get the list. That also implies that if you are adding a node to the server cluster AFTER the client’s initial lookup, the client would not know it because neither the server nor the client will initiate a refresh of the “list of available servers” to reflect the new node. About High Availability (i.e. Resilience Against Node Failure of Remote OC4J Cluster) What we have discussed above is about load balancing. Let's also discuss high availability. This is how the High Availability works in RMI: when the client use the context but get an exception such as socket is closed, it knows that the server referenced by that Context is problematic and will try to get another unused Context from the “list of available contexts”. Again, this list is the list that was returned and cached at the very first lookup in the entire client session.

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  • People, Process & Engagement: WebCenter Partner Keste

    - by Michael Snow
    v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Within the WebCenter group here at Oracle, discussions about people, process and engagement cross over many vertical industries and products. Amidst our growing partner ecosystem, the community provides us insight into great customer use cases every day. Such is the case with our partner, Keste, who provides us a guest post on our blog today with an overview of their innovative solution for a customer in the transportation industry. Keste is an Oracle software solutions and development company headquartered in Dallas, Texas. As a Platinum member of the Oracle® PartnerNetwork, Keste designs, develops and deploys custom solutions that automate complex business processes. Seamless Customer Self-Service Experience in the Trucking Industry with Oracle WebCenter Portal  Keste, Oracle Platinum Partner Customer Overview Omnitracs, Inc., a Qualcomm company provides mobility solutions for trucking fleets to companies in the transportation industry. Omnitracs’ mobility services include basic communications such as text as well as advanced monitoring services such as GPS tracking, temperature tracking of perishable goods, load tracking and weighting distribution, and many others. Customer Business Needs Already the leading provider of mobility solutions for large trucking fleets, they chose to target smaller trucking fleets as new customers. However their existing high-touch customer support method would not be a cost effective or scalable method to manage and service these smaller customers. Omnitracs needed to provide several self-service features to make customer support more scalable while keeping customer satisfaction levels high and the costs manageable. The solution also had to be very intuitive and easy to use. The systems that Omnitracs sells to these trucking customers require professional installation and smaller customers need to track and schedule the installation. Information captured in Oracle eBusiness Suite needed to be readily available for new customers to track these purchases and delivery details. Omnitracs wanted a high impact User Interface to significantly improve customer experience with the ability to integrate with EBS, provisioning systems as well as CRM systems that were already implemented. Omnitracs also wanted to build an architecture platform that could potentially be extended to other Portals. Omnitracs’ stated goal was to deliver an “eBay-like” or “Amazon-like” experience for all of their customers so that they could reach a much broader market beyond their large company customer base. Solution Overview In order to manage the increased complexity, the growing support needs of global customers and improve overall product time-to-market in a cost-effective manner, IT began to deliver a self-service model. This self service model not only transformed numerous business processes but is also allowing the business to keep up with the growing demands of the (internal and external) customers. This solution was a customer service Portal that provided self service capabilities for large and small customers alike for Activation of mobility products, managing add-on applications for the devices (much like the Apple App Store), transferring services when trucks are sold to other companies as well as deactivation all without the involvement of a call service agent or sending multiple emails to different Omnitracs contacts. This is a conceptual view of the Customer Portal showing the details of the components that make up the solution. 12.00 The portal application for transactions was entirely built using ADF 11g R2. Omnitracs’ business had a pressing requirement to have a portal available 24/7 for its customers. Since there were interactions with EBS in the back-end, the downtimes on the EBS would negate this availability. Omnitracs devised a decoupling strategy at the database side for the EBS data. The decoupling of the database was done using Oracle Data Guard and completely insulated the solution from any eBusiness Suite down time. The customer has no knowledge whether eBS is running or not. Here are two sample screenshots of the portal application built in Oracle ADF. Customer Benefits The Customer Portal not only provided the scalability to grow the business but also provided the seamless integration with other disparate applications. Some of the key benefits are: Improved Customer Experience: With a modern look and feel and a Portal that has the aspects of an App Store, the customer experience was significantly improved. Page response times went from several seconds to sub-second for all of the pages. Enabled new product launches: After successfully dominating the large fleet market, Omnitracs now has a scalable solution to sell and manage smaller fleet customers giving them a huge advantage over their nearest competitors. Dozens of new customers have been acquired via this portal through an onboarding process that now takes minutes Seamless Integrations Improves Customer Support: ADF 11gR2 allowed Omnitracs to bring a diverse list of applications into one integrated solution. This provided a seamless experience for customers to route them from Marketing focused application to a customer-oriented portal. Internally, it also allowed Sales Representatives to have an integrated flow for taking a prospect through the various steps to onboard them as a customer. Key integrations included: Unity Core Salesforce.com Merchant e-Solution for credit card Custom Omnitracs Applications like CUPS and AUTO Security utilizing OID and OVD Back end integration with EBS (Data Guard) and iQ Database Business Impact Significant business impacts were realized through the launch of customer portal. It not only allows the business to push through in underserved segments, but also reduces the time it needs to spend on customer support—allowing the business to focus more on sales and identifying the market for new products. Some of the Immediate Benefits are The entire onboarding process is now completely automated and now completes in minutes. This represents an 85% productivity improvement over their previous processes. And it was 160 times faster! With the success of this self-service solution, the business is now targeting about 3X customer growth in the next five years. This represents a tripling of their overall customer base and significant downstream revenue for the ongoing services. 90%+ improvement of customer onboarding and management process by utilizing, single sign on integration using OID/OAM solution, performance improvements and new self-service functionality Unified login for all Customers, Partners and Internal Users enables login to a common portal and seamless access to all other integrated applications targeted at the respective audience Significantly improved customer experience with a better look and feel with a more user experience focused Portal screens. Helped sales of the new product by having an easy way of ordering and activating the product. Data Guard helped increase availability of the Portal to 99%+ and make it independent of EBS downtime. This gave customers the feel of high availability of the portal application. Some of the anticipated longer term Benefits are: Platform that can be leveraged to launch any new product introduction and enable all product teams to reach new customers and new markets Easy integration with content management to allow business owners more control of the product catalog Overall reduced TCO with standardization of the Oracle platform Managed IT support cost savings through optimization of technology skills needed to support and modify this solution ------------------------------------------------------------ 12.00 Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 -"/ /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Times New Roman","serif";}

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  • Integrating Windows Form Click Once Application into SharePoint 2007 &ndash; Part 2 of 4

    - by Kelly Jones
    In my last post, I explained why we decided to use a Click Once application to solve our business problem. To quickly review, we needed a way for our business users to upload documents to a SharePoint 2007 document library in mass, set the meta data, set the permissions per document, and to do so easily. Let’s look at the pieces that make up our solution.  First, we have the Windows Form application.  This app is deployed using Click Once and calls SharePoint web services in order to upload files and then calls web services to set the meta data (SharePoint columns and permissions).  Second, we have a custom action.  The custom action is responsible for providing our users a link that will launch the Windows app, as well as passing values to it via the query string.  And lastly, we have the web services that the Windows Form application calls.  For our solution, we used both out of the box web services and a custom web service in order to set the column values in the document library as well as the permissions on the documents. Now, let’s look at the technical details of each of these pieces.  (All of the code is downloadable from here: )   Windows Form application deployed via Click Once The Windows Form application, called “Custom Upload”, has just a few classes in it: Custom Upload -- the form FileList.xsd -- the dataset used to track the names of the files and their meta data values SharePointUpload -- this class handles uploading the file SharePointUpload uses an HttpWebRequest to transfer the file to the web server. We had to change this code from a WebClient object to the HttpWebRequest object, because we needed to be able to set the time out value.  public bool UploadDocument(string localFilename, string remoteFilename) { bool result = true; //Need to use an HttpWebRequest object instead of a WebClient object // so we can set the timeout (WebClient doesn't allow you to set the timeout!) HttpWebRequest req = (HttpWebRequest)WebRequest.Create(remoteFilename); try { req.Method = "PUT"; req.Timeout = 60 * 1000; //convert seconds to milliseconds req.AllowWriteStreamBuffering = true; req.Credentials = System.Net.CredentialCache.DefaultCredentials; req.SendChunked = false; req.KeepAlive = true; Stream reqStream = req.GetRequestStream(); FileStream rdr = new FileStream(localFilename, FileMode.Open, FileAccess.Read); byte[] inData = new byte[4096]; int bytesRead = rdr.Read(inData, 0, inData.Length); while (bytesRead > 0) { reqStream.Write(inData, 0, bytesRead); bytesRead = rdr.Read(inData, 0, inData.Length); } reqStream.Close(); rdr.Close(); System.Net.HttpWebResponse response = (HttpWebResponse)req.GetResponse(); if (response.StatusCode != HttpStatusCode.OK && response.StatusCode != HttpStatusCode.Created) { String msg = String.Format("An error occurred while uploading this file: {0}\n\nError response code: {1}", System.IO.Path.GetFileName(localFilename), response.StatusCode.ToString()); LogWarning(msg, "2ACFFCCA-59BA-40c8-A9AB-05FA3331D223"); result = false; } } catch (Exception ex) { LogException(ex, "{E9D62A93-D298-470d-A6BA-19AAB237978A}"); result = false; } return result; } The class also contains the LogException() and LogWarning() methods. When the application is launched, it parses the query string for some initial values.  The query string looks like this: string queryString = "Srv=clickonce&Sec=N&Doc=DMI&SiteName=&Speed=128000&Max=50"; This Srv is the path to the server (my Virtual Machine is name “clickonce”), the Sec is short for security – meaning HTTPS or HTTP, the Doc is the shortcut for which document library to use, and SiteName is the name of the SharePoint site.  Speed is used to calculate an estimate for download speed for each file.  We added this so our users uploading documents would realize how long it might take for clients in remote locations (using slow WAN connections) to download the documents. The last value, Max, is the maximum size that the SharePoint site will allow documents to be.  This allowed us to give users a warning that a file is too large before we even attempt to upload it. Another critical piece is the meta data collection.  We organized our site using SharePoint content types, so when the app loads, it gets a list of the document library’s content types.  The user then select one of the content types from the drop down list, and then we query SharePoint to get a list of the fields that make up that content type.  We used both an out of the box web service, and one that we custom built, in order to get these values. Once we have the content type fields, we then add controls to the form.  Which type of control we add depends on the data type of the field.  (DateTime pickers for date/time fields, etc)  We didn’t write code to cover every data type, since we were working with a limited set of content types and field data types. Here’s a screen shot of the Form, before and after someone has selected the content types and our code has added the custom controls:     The other piece of meta data we collect is the in the upper right corner of the app, “Users with access”.  This box lists the different SharePoint Groups that we have set up and by checking the boxes, the user can set the permissions on the uploaded documents. All of this meta data is collected and submitted to our custom web service, which then sets the values on the documents on the list.  We’ll look at these web services in a future post. In the next post, we’ll walk through the Custom Action we built.

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  • XNA RTS A* pathfinding issues

    - by Slayter
    I'm starting to develop an RTS game using the XNA framework in C# and am still in the very early prototyping stage. I'm working on the basics. I've got unit selection down and am currently working on moving multiple units. I've implemented an A* pathfinding algorithm which works fine for moving a single unit. However when moving multiple units they stack on top of each other. I tried fixing this with a variation of the boids flocking algorithm but this has caused units to sometimes freeze and get stuck trying to move but going no where. Ill post the related methods for moving the units below but ill only post a link to the pathfinding class because its really long and i don't want to clutter up the page. These parts of the code are in the update method for the main controlling class: if (selectedUnits.Count > 0) { int indexOfLeader = 0; for (int i = 0; i < selectedUnits.Count; i++) { if (i == 0) { indexOfLeader = 0; } else { if (Vector2.Distance(selectedUnits[i].position, destination) < Vector2.Distance(selectedUnits[indexOfLeader].position, destination)) indexOfLeader = i; } selectedUnits[i].leader = false; } selectedUnits[indexOfLeader].leader = true; foreach (Unit unit in selectedUnits) unit.FindPath(destination); } foreach (Unit unit in units) { unit.Update(gameTime, selectedUnits); } These three methods control movement in the Unit class: public void FindPath(Vector2 destination) { if (path != null) path.Clear(); Point startPoint = new Point((int)position.X / 32, (int)position.Y / 32); Point endPoint = new Point((int)destination.X / 32, (int)destination.Y / 32); path = pathfinder.FindPath(startPoint, endPoint); pointCounter = 0; if (path != null) nextPoint = path[pointCounter]; dX = 0.0f; dY = 0.0f; stop = false; } private void Move(List<Unit> units) { if (nextPoint == position && !stop) { pointCounter++; if (pointCounter <= path.Count - 1) { nextPoint = path[pointCounter]; if (nextPoint == position) stop = true; } else if (pointCounter >= path.Count) { path.Clear(); pointCounter = 0; stop = true; } } else { if (!stop) { map.occupiedPoints.Remove(this); Flock(units); // Move in X ********* TOOK OUT SPEED ********** if ((int)nextPoint.X > (int)position.X) { position.X += dX; } else if ((int)nextPoint.X < (int)position.X) { position.X -= dX; } // Move in Y if ((int)nextPoint.Y > (int)position.Y) { position.Y += dY; } else if ((int)nextPoint.Y < (int)position.Y) { position.Y -= dY; } if (position == nextPoint && pointCounter >= path.Count - 1) stop = true; map.occupiedPoints.Add(this, position); } if (stop) { path.Clear(); pointCounter = 0; } } } private void Flock(List<Unit> units) { float distanceToNextPoint = Vector2.Distance(position, nextPoint); foreach (Unit unit in units) { float distance = Vector2.Distance(position, unit.position); if (unit != this) { if (distance < space && !leader && (nextPoint != position)) { // create space dX += (position.X - unit.position.X) * 0.1f; dY += (position.Y - unit.position.Y) * 0.1f; if (dX > .05f) nextPoint.X = nextPoint.X - dX; else if (dX < -.05f) nextPoint.X = nextPoint.X + dX; if (dY > .05f) nextPoint.Y = nextPoint.Y - dY; else if (dY < -.05f) nextPoint.Y = nextPoint.Y + dY; if ((dX < .05f && dX > -.05f) && (dY < .05f && dY > -.05f)) stop = true; path[pointCounter] = nextPoint; Console.WriteLine("Make Space: " + dX + ", " + dY); } else if (nextPoint != position && !stop) { dX = speed; dY = speed; Console.WriteLine(dX + ", " + dY); } } } } And here's the link to the pathfinder: https://docs.google.com/open?id=0B_Cqt6txUDkddU40QXBMeTR1djA I hope this post wasn't too long. Also please excuse the messiness of the code. As I said before this is early prototyping. Any help would be appreciated. Thanks!

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  • Best Practices - which domain types should be used to run applications

    - by jsavit
    This post is one of a series of "best practices" notes for Oracle VM Server for SPARC (formerly named Logical Domains) One question that frequently comes up is "which types of domain should I use to run applications?" There used to be a simple answer in most cases: "only run applications in guest domains", but enhancements to T-series servers, Oracle VM Server for SPARC and the advent of SPARC SuperCluster have made this question more interesting and worth qualifying differently. This article reviews the relevant concepts and provides suggestions on where to deploy applications in a logical domains environment. Review: division of labor and types of domain Oracle VM Server for SPARC offloads many functions from the hypervisor to domains (also called virtual machines). This is a modern alternative to using a "thick" hypervisor that provides all virtualization functions, as in traditional VM designs, This permits a simpler hypervisor design, which enhances reliability, and security. It also reduces single points of failure by assigning responsibilities to multiple system components, which further improves reliability and security. In this architecture, management and I/O functionality are provided within domains. Oracle VM Server for SPARC does this by defining the following types of domain, each with their own roles: Control domain - management control point for the server, used to configure domains and manage resources. It is the first domain to boot on a power-up, is an I/O domain, and is usually a service domain as well. I/O domain - has been assigned physical I/O devices: a PCIe root complex, a PCI device, or a SR-IOV (single-root I/O Virtualization) function. It has native performance and functionality for the devices it owns, unmediated by any virtualization layer. Service domain - provides virtual network and disk devices to guest domains. Guest domain - a domain whose devices are all virtual rather than physical: virtual network and disk devices provided by one or more service domains. In common practice, this is where applications are run. Typical deployment A service domain is generally also an I/O domain: otherwise it wouldn't have access to physical device "backends" to offer to its clients. Similarly, an I/O domain is also typically a service domain in order to leverage the available PCI busses. Control domains must be I/O domains, because they boot up first on the server and require physical I/O. It's typical for the control domain to also be a service domain too so it doesn't "waste" the I/O resources it uses. A simple configuration consists of a control domain, which is also the one I/O and service domain, and some number of guest domains using virtual I/O. In production, customers typically use multiple domains with I/O and service roles to eliminate single points of failure: guest domains have virtual disk and virtual devices provisioned from more than one service domain, so failure of a service domain or I/O path or device doesn't result in an application outage. This is also used for "rolling upgrades" in which service domains are upgraded one at a time while their guests continue to operate without disruption. (It should be noted that resiliency to I/O device failures can also be provided by the single control domain, using multi-path I/O) In this type of deployment, control, I/O, and service domains are used for virtualization infrastructure, while applications run in guest domains. Changing application deployment patterns The above model has been widely and successfully used, but more configuration options are available now. Servers got bigger than the original T2000 class machines with 2 I/O busses, so there is more I/O capacity that can be used for applications. Increased T-series server capacity made it attractive to run more vertical applications, such as databases, with higher resource requirements than the "light" applications originally seen. This made it attractive to run applications in I/O domains so they could get bare-metal native I/O performance. This is leveraged by the SPARC SuperCluster engineered system, announced a year ago at Oracle OpenWorld. In SPARC SuperCluster, I/O domains are used for high performance applications, with native I/O performance for disk and network and optimized access to the Infiniband fabric. Another technical enhancement is the introduction of Direct I/O (DIO) and Single Root I/O Virtualization (SR-IOV), which make it possible to give domains direct connections and native I/O performance for selected I/O devices. A domain with either a DIO or SR-IOV device is an I/O domain. In summary: not all I/O domains own PCI complexes, and there are increasingly more I/O domains that are not service domains. They use their I/O connectivity for performance for their own applications. However, there are some limitations and considerations: at this time, a domain using physical I/O cannot be live-migrated to another server. There is also a need to plan for security and introducing unneeded dependencies: if an I/O domain is also a service domain providing virtual I/O go guests, it has the ability to affect the correct operation of its client guest domains. This is even more relevant for the control domain. where the ldm has to be protected from unauthorized (or even mistaken) use that would affect other domains. As a general rule, running applications in the service domain or the control domain should be avoided. To recap: Guest domains with virtual I/O still provide the greatest operational flexibility, including features like live migration. I/O domains can be used for applications with high performance requirements. This is used to great effect in SPARC SuperCluster and in general T4 deployments. Direct I/O (DIO) and Single Root I/O Virtualization (SR-IOV) make this more attractive by giving direct I/O access to more domains. Service domains should in general not be used for applications, because compromised security in the domain, or an outage, can affect other domains that depend on it. This concern can be mitigated by providing guests' their virtual I/O from more than one service domain, so an interruption of service in the service domain does not cause an application outage. The control domain should in general not be used to run applications, for the same reason. SPARC SuperCluster use the control domain for applications, but it is an exception: it's not a general purpose environment; it's an engineered system with specifically configured applications and optimization for optimal performance. These are recommended "best practices" based on conversations with a number of Oracle architects. Keep in mind that "one size does not fit all", so you should evaluate these practices in the context of your own requirements. Summary Higher capacity T-series servers have made it more attractive to use them for applications with high resource requirements. New deployment models permit native I/O performance for demanding applications by running them in I/O domains with direct access to their devices. This is leveraged in SPARC SuperCluster, and can be leveraged in T-series servers to provision high-performance applications running in domains. Carefully planned, this can be used to provide higher performance for critical applications.

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