high_scalability high_scalability-2007 high_scalability-2007-164 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: I have some experience with a very large OLTP system that is 7+ TB in size and performs very well for 30K+ concurrent users. It is built using Intersystems Cache based on the very old but very scalable MUMPS platform. Why don't I see more discussions about archiectures such as these in this forum? I am curious why this platform scales so much better then the typical RDBMS.
sentIndex sentText sentNum sentScore
1 I have some experience with a very large OLTP system that is 7+ TB in size and performs very well for 30K+ concurrent users. [sent-1, score-0.907]
2 It is built using Intersystems Cache based on the very old but very scalable MUMPS platform. [sent-2, score-0.474]
3 Why don't I see more discussions about archiectures such as these in this forum? [sent-3, score-0.381]
4 I am curious why this platform scales so much better then the typical RDBMS. [sent-4, score-0.928]
wordName wordTfidf (topN-words)
[('mumps', 0.493), ('discussions', 0.302), ('curious', 0.287), ('forum', 0.279), ('oltp', 0.269), ('tb', 0.255), ('performs', 0.252), ('rdbms', 0.246), ('concurrent', 0.191), ('scales', 0.174), ('typical', 0.172), ('old', 0.147), ('size', 0.143), ('platform', 0.133), ('cache', 0.113), ('experience', 0.112), ('built', 0.11), ('scalable', 0.089), ('better', 0.087), ('well', 0.083), ('based', 0.082), ('see', 0.079), ('large', 0.076), ('much', 0.075), ('system', 0.05), ('using', 0.046)]
simIndex simValue blogId blogTitle
same-blog 1 1.0 164 high scalability-2007-11-22-Why not Cache from Intersystems?
Introduction: I have some experience with a very large OLTP system that is 7+ TB in size and performs very well for 30K+ concurrent users. It is built using Intersystems Cache based on the very old but very scalable MUMPS platform. Why don't I see more discussions about archiectures such as these in this forum? I am curious why this platform scales so much better then the typical RDBMS.
2 0.12136698 752 high scalability-2009-12-17-Oracle and IBM databases: Disk-based vs In-memory databases
Introduction: Current disk based RDBMS can run out of steam when processing large data. Can these problems be solved by migrating from a disk based RDBMS to an IMDB? Any limitations? To find out, I tested one of each from the two leading vendors who together hold 70% of the market share - Oracle's 11g and TimesTen 11g , and IBM's DB2 v9.5 and solidDB 6.3 . read more at BigDataMatters.com
3 0.11410557 467 high scalability-2008-12-16-[ANN] New Open Source Cache System
Introduction: The SHOP.COM Cache System is now available at http://code.google.com/p/sccache/ The SHOP.COM Cache System is an object cache system that... * is an in-process cache and external, shared Cache * is horizontally scalable * stores cached objects to disk * supports associative keys * is non-transactional * can have any size key and any size data * does auto-GC based on TTL * is container and platform neutral It was built in-house at SHOP.COM (by me) and has powered our website for years. We are open-sourcing it in the hope that it will be useful to others and to get some help in its maintenance. This is our first open source attempt and we'd appreciate any help and comments.
4 0.10515037 211 high scalability-2008-01-13-Google Reveals New MapReduce Stats
Introduction: The Google Operating System blog has an interesting post on Google's scale based on an updated version of Google's paper about MapReduce. The input data for some of the MapReduce jobs run in September 2007 was 403,152 TB (terabytes), the average number of machines allocated for a MapReduce job was 394, while the average completion time was 6 minutes and a half. The paper mentions that Google's indexing system processes more than 20 TB of raw data. Niall Kennedy calculates that the average MapReduce job runs across a $1 million hardware infrastructure, assuming that Google still uses the same cluster configurations from 2004: two 2 GHz Intel Xeon processors with Hyper-Threading enabled, 4 GB of memory, two 160 GB IDE hard drives and a gigabit Ethernet link. Greg Linden notices that Google's infrastructure is an important competitive advantage. "Anyone at Google can process terabytes of data. And they can get their results back in about 10 minutes, so they ca
5 0.10342938 572 high scalability-2009-04-16-Paper: The End of an Architectural Era (It’s Time for a Complete Rewrite)
Introduction: Update 3 : A Comparison of Approaches to Large-Scale Data Analysis: MapReduce vs. DBMS Benchmarks . Although the process to load data into and tune the execution of parallel DBMSs took much longer than the MR system, the observed performance of these DBMSs was strikingly better. Update 2 : H-Store: A Next Generation OLTP DBMS is the project implementing the ideas in this paper: The goal of the H-Store project is to investigate how these architectural and application shifts affect the performance of OLTP databases, and to study what performance benefits would be possible with a complete redesign of OLTP systems in light of these trends. Our early results show that a simple prototype built from scratch using modern assumptions can outperform current commercial DBMS offerings by around a factor of 80 on OLTP workloads. Update : interesting related thread on Lamda the Ultimate . A really fascinating paper bolstering many of the anti-RDBMS threads the have popped up on the intert
6 0.098741338 370 high scalability-2008-08-18-Forum sort order
7 0.078300513 848 high scalability-2010-06-25-Hot Scalability Links for June 25, 2010
8 0.076507777 620 high scalability-2009-06-05-SSL RPC API Scalability
9 0.07626541 679 high scalability-2009-08-11-13 Scalability Best Practices
10 0.07433594 450 high scalability-2008-11-24-Scalability Perspectives #3: Marc Andreessen – Internet Platforms
11 0.071150824 1364 high scalability-2012-11-29-Performance data for LevelDB, Berkley DB and BangDB for Random Operations
12 0.070434287 140 high scalability-2007-11-02-How WordPress.com Tracks 300 Servers Handling 10 Million Pageviews
13 0.069662638 693 high scalability-2009-09-03-Storage Systems for High Scalable Systems presentation
14 0.068647981 1305 high scalability-2012-08-16-Paper: A Provably Correct Scalable Concurrent Skip List
15 0.066497594 83 high scalability-2007-09-07-Joost Network Architecture
16 0.066029415 542 high scalability-2009-03-17-IBM WebSphere eXtreme Scale (IMDG)
17 0.065267719 251 high scalability-2008-02-18-How to deal with an I-O bottleneck to disk?
18 0.062371805 1199 high scalability-2012-02-27-Zen and the Art of Scaling - A Koan and Epigram Approach
19 0.06178781 821 high scalability-2010-05-03-MocoSpace Architecture - 3 Billion Mobile Page Views a Month
20 0.059604988 360 high scalability-2008-08-04-A Bunch of Great Strategies for Using Memcached and MySQL Better Together
topicId topicWeight
[(0, 0.076), (1, 0.042), (2, 0.0), (3, -0.009), (4, 0.003), (5, 0.038), (6, 0.002), (7, -0.012), (8, -0.03), (9, 0.016), (10, -0.026), (11, -0.036), (12, 0.015), (13, 0.038), (14, -0.022), (15, -0.017), (16, -0.024), (17, -0.06), (18, 0.029), (19, 0.005), (20, -0.02), (21, 0.016), (22, 0.022), (23, 0.012), (24, -0.024), (25, -0.023), (26, 0.008), (27, 0.002), (28, 0.02), (29, 0.002), (30, -0.003), (31, 0.014), (32, -0.024), (33, -0.017), (34, -0.018), (35, 0.051), (36, -0.013), (37, 0.008), (38, 0.024), (39, 0.041), (40, 0.001), (41, 0.011), (42, 0.03), (43, -0.015), (44, -0.011), (45, 0.062), (46, -0.008), (47, -0.025), (48, 0.061), (49, 0.032)]
simIndex simValue blogId blogTitle
same-blog 1 0.95028526 164 high scalability-2007-11-22-Why not Cache from Intersystems?
Introduction: I have some experience with a very large OLTP system that is 7+ TB in size and performs very well for 30K+ concurrent users. It is built using Intersystems Cache based on the very old but very scalable MUMPS platform. Why don't I see more discussions about archiectures such as these in this forum? I am curious why this platform scales so much better then the typical RDBMS.
2 0.6617049 467 high scalability-2008-12-16-[ANN] New Open Source Cache System
Introduction: The SHOP.COM Cache System is now available at http://code.google.com/p/sccache/ The SHOP.COM Cache System is an object cache system that... * is an in-process cache and external, shared Cache * is horizontally scalable * stores cached objects to disk * supports associative keys * is non-transactional * can have any size key and any size data * does auto-GC based on TTL * is container and platform neutral It was built in-house at SHOP.COM (by me) and has powered our website for years. We are open-sourcing it in the hope that it will be useful to others and to get some help in its maintenance. This is our first open source attempt and we'd appreciate any help and comments.
3 0.65910953 1467 high scalability-2013-05-30-Google Finds NUMA Up to 20% Slower for Gmail and Websearch
Introduction: When you have a large population of servers you have both the opportunity and the incentive to perform interesting studies. Authors from Google and the University of California in Optimizing Google’s Warehouse Scale Computers: The NUMA Experience conducted such a study, taking a look at how jobs run on clusters of machines using a NUMA architecture. Since NUMA is common on server class machines it's a topic of general interest for those looking to maximize machine utilization across clusters. Some of the results are surprising: The methodology of how to attribute such fine performance variations to NUMA effects within such a complex system is perhaps more interesting than the results themselves. Well worth reading just for that story. The performance swing due to NUMA is up to 15% on AMD Barcelona for Gmail backend and 20% on Intel Westmere for Web-search frontend. Memory locality is not always King. Because of the interaction between NUMA and cache sharing/contention it
4 0.64710212 701 high scalability-2009-09-10-When optimizing - don't forget the Java Virtual Machine (JVM)
Introduction: Recently, I was working on a project that was coming to a close. It was related to optimizing a database using a Java based in-memory cache to reduce the load. The application had to process up to a million objects per day and was characterized by its heavy use of memory and the high number of read, write and update operations. These operations were found to be the most costly, which meant that optimization efforts were concentrated here. The project had already achieved impressive performance increases, but one question remained unanswered - would changing the JVM increase performance? Read more at: http://bigdatamatters.com/bigdatamatters/2009/08/jvm-performance.html
5 0.62566972 1246 high scalability-2012-05-16-Big List of 20 Common Bottlenecks
Introduction: In Zen And The Art Of Scaling - A Koan And Epigram Approach , Russell Sullivan offered an interesting conjecture: there are 20 classic bottlenecks. This sounds suspiciously like the idea that there only 20 basic story plots . And depending on how you chunkify things, it may be true, but in practice we all know bottlenecks come in infinite flavors, all tasting of sour and ash. One day Aurelien Broszniowski from Terracotta emailed me his list of bottlenecks, we cc’ed Russell in on the conversation, he gave me his list, I have a list, and here’s the resulting stone soup. Russell said this is his “I wish I knew when I was younger" list and I think that’s an enriching way to look at it. The more experience you have, the more different types of projects you tackle, the more lessons you’ll be able add to a list like this. So when you read this list, and when you make your own, you are stepping through years of accumulated experience and more than a little frustration, but in ea
6 0.62426323 149 high scalability-2007-11-12-Scaling Using Cache Farms and Read Pooling
7 0.62150365 1364 high scalability-2012-11-29-Performance data for LevelDB, Berkley DB and BangDB for Random Operations
8 0.61724126 1471 high scalability-2013-06-06-Paper: Memory Barriers: a Hardware View for Software Hackers
9 0.61308002 708 high scalability-2009-09-17-Infinispan narrows the gap between open source and commercial data caches
10 0.61057079 1541 high scalability-2013-10-31-Paper: Everything You Always Wanted to Know About Synchronization but Were Afraid to Ask
11 0.61031848 696 high scalability-2009-09-07-Product: Infinispan - Open Source Data Grid
12 0.60789156 1582 high scalability-2014-01-20-8 Ways Stardog Made its Database Insanely Scalable
13 0.60265285 174 high scalability-2007-12-05-Product: Tugela Cache
14 0.60204774 1236 high scalability-2012-04-30-Masstree - Much Faster than MongoDB, VoltDB, Redis, and Competitive with Memcached
15 0.5937525 359 high scalability-2008-07-29-Ehcache - A Java Distributed Cache
16 0.5853861 577 high scalability-2009-04-22-Gear6 Web cache - the hardware solution for working with Memcache
17 0.5811035 1620 high scalability-2014-03-27-Strategy: Cache Stored Procedure Results
18 0.57914549 859 high scalability-2010-07-14-DynaTrace's Top 10 Performance Problems taken from Zappos, Monster, Thomson and Co
19 0.5766241 572 high scalability-2009-04-16-Paper: The End of an Architectural Era (It’s Time for a Complete Rewrite)
20 0.57533258 421 high scalability-2008-10-17-A High Performance Memory Database for Web Application Caches
topicId topicWeight
[(2, 0.343), (52, 0.324), (79, 0.152)]
simIndex simValue blogId blogTitle
same-blog 1 0.86337847 164 high scalability-2007-11-22-Why not Cache from Intersystems?
Introduction: I have some experience with a very large OLTP system that is 7+ TB in size and performs very well for 30K+ concurrent users. It is built using Intersystems Cache based on the very old but very scalable MUMPS platform. Why don't I see more discussions about archiectures such as these in this forum? I am curious why this platform scales so much better then the typical RDBMS.
2 0.85537452 872 high scalability-2010-08-05-Pairing NoSQL and Relational Data Storage: MySQL with MongoDB
Introduction: I’ve largely steered clear of publicly commenting on the “NoSQL vs. Relational” conflict. Keeping in mind that this argument is more about currently available solutions and the features their developers have chosen to build in, I’d like to dig into this and provide a decidedly neutral viewpoint. In fact, by erring on the side of caution, I’ve inadvertently given myself plenty of time to consider the pros and cons of both data storage approaches, and although my mind was initially swaying toward the NoSQL camp, I can say with a fair amount of certainty, that I’ve found a good compromise. You can read the full store here .
Introduction: Scoble the Ubiquitous has a fascinating post on how Mogulus, a live video channel startup, uses S3/EC2 and doesn't own a single server. The trends that have been happening for a while now are going mainstream. To do great things you no longer need to start by creating a huge war chest. You can forage off the land, like any good mobile, light weight fighting unit. For a strategy hit he mentions the same needed change in perspective as Beau Lebens talked about when making FeedBlendr : One tip he gave us is that when using Amazon’s services you have to design your systems with the assumption that they will never be up and running. What he means by that is services are “volatile” and can go up and down without notice. So, he’s designed his systems to survive that. He told me that it meant his engineering teams had to be quite disciplined in designing their architecture.
4 0.80021554 1406 high scalability-2013-02-14-When all the Program's a Graph - Prismatic's Plumbing Library
Introduction: At some point as a programmer you might have the insight/fear that all programming is just doing stuff to other stuff. Then you may observe after coding the same stuff over again that stuff in a program often takes the form of interacting patterns of flows. Then you may think hey, a program isn't only useful for coding datastructures, but a program is a kind of datastructure and that with a meta level jump you could program a program in terms of flows over data and flow over other flows. That's the kind of stuff Prismatic is making available in the Graph extension to their plumbing package ( code examples ), which is described in an excellent post: Graph: Abstractions for Structured Computation . You may remember Prismatic from previous profile we did on HighScalability: Prismatic Architecture - Using Machine Learning On Social Networks To Figure Out What You Should Read On The Web . We learned how Prismatic, an interest driven content suggestion service, builds programs in
5 0.79510975 47 high scalability-2007-07-30-Product: Yslow to speed up your web pages
Introduction: Update : Speed up Apache - how I went from F to A in YSlow . Good example of using YSlow to speed up a website with solid code examples. Every layer in the multi-layer cake that is your website contributes to how long a page takes to display. YSlow , from Yahoo, is a cool tool for discovering how the ingredients of your site's top layer contribute to performance. YSlow analyzes web pages and tells you why they're slow based on the rules for high performance web sites. YSlow is a Firefox add-on integrated with the popular Firebug web development tool. YSlow gives you: Performance report card HTTP/HTML summary List of components in the page Tools including JSLint
6 0.74612594 244 high scalability-2008-02-11-Yahoo Live's Scaling Problems Prove: Release Early and Often - Just Don't Screw Up
7 0.73433709 230 high scalability-2008-01-29-Speed up (Oracle) database code with result caching
8 0.73424464 417 high scalability-2008-10-15-Outside.in Scales Up with Engine Yard and moving from PHP to Ruby on Rails
9 0.73268032 882 high scalability-2010-08-18-Misco: A MapReduce Framework for Mobile Systems - Start of the Ambient Cloud?
10 0.72775972 50 high scalability-2007-07-31-BerkeleyDB & other distributed high performance key-value databases
12 0.71603936 359 high scalability-2008-07-29-Ehcache - A Java Distributed Cache
13 0.71551585 1558 high scalability-2013-12-04-How Can Batching Requests Actually Reduce Latency?
14 0.71502036 910 high scalability-2010-09-30-Facebook and Site Failures Caused by Complex, Weakly Interacting, Layered Systems
15 0.71238303 1266 high scalability-2012-06-18-Google on Latency Tolerant Systems: Making a Predictable Whole Out of Unpredictable Parts
17 0.70987236 120 high scalability-2007-10-11-How Flickr Handles Moving You to Another Shard
18 0.70964503 1568 high scalability-2013-12-23-What Happens While Your Brain Sleeps is Surprisingly Like How Computers Stay Sane
19 0.70933455 862 high scalability-2010-07-20-Strategy: Consider When a Service Starts Billing in Your Algorithm Cost
20 0.70794261 52 high scalability-2007-08-01-Product: Memcached