high_scalability high_scalability-2009 high_scalability-2009-589 knowledge-graph by maker-knowledge-mining

589 high scalability-2009-05-05-Drop ACID and Think About Data


meta infos for this blog

Source: html

Introduction: The abstract for the talk given by Bob Ippolito, co-founder and CTO of Mochi Media, Inc: Building large systems on top of a traditional single-master RDBMS data storage layer is no longer good enough. This talk explores the landscape of new technologies available today to augment your data layer to improve performance and reliability. Is your application a good fit for caches, bloom filters, bitmap indexes, column stores, distributed key/value stores, or document databases? Learn how they work (in theory and practice) and decide for yourself. Bob does an excellent job highlighting different products and the key concepts to understand when pondering the wide variety of new database offerings. It's unlikely you'll be able to say oh, this is the database for me after watching the presentation, but you will be much better informed on your options. And I imagine slightly confused as to what to do :-) An interesting observation in the talk is that the more robust products are internal


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 Is your application a good fit for caches, bloom filters, bitmap indexes, column stores, distributed key/value stores, or document databases? [sent-3, score-0.628]

2 From all the choices discussed the column database Vertica seems closest to Bob's heart and it's the product they use. [sent-10, score-0.422]

3 It supports clustering, column storage, compression, bitmapped indexes, bloom filters, grids, and lots of other useful features. [sent-11, score-0.54]

4 Availability - you can read and write and write your data all the time Partition Tolerance - if one or more nodes fails the system still works and becomes consistent when the system comes on-line. [sent-22, score-0.719]

5 compression - great gains in throughput, can store more, reduces IO bottleneck because you have to store less so you have to talk to the disks less so performance improves. [sent-27, score-0.755]

6 hybrid between row and column database row database - store objects together column database - store attributes of objects together. [sent-29, score-1.642]

7 versioning bloom filters - allows data to be distributed across a bunch of nodes. [sent-31, score-0.42]

8 It's a calculation on data that probabilistically maps the data to the nodes it can be found on. [sent-32, score-0.482]

9 Uses consistent hashing to distribute data to one or more nodes for redundancy and performance. [sent-42, score-0.528]

10 Consistent hashing - a ring of nodes and hash function picks which node(s) to store data Consistency between nodes is based on vector clocks and read repair. [sent-43, score-0.9]

11 Read repair - When a client does a read and the nodes disagree on the data it's up to the client to select the correct data and tell the nodes the new correct state. [sent-45, score-0.922]

12 You can write to many nodes at once so depending on the number of replicas (which is configurable) maintained you should always be able to write somewhere. [sent-48, score-0.437]

13 It's a data structure store not a key-value store, which means it understands your values so you can operate on them. [sent-96, score-0.45]

14 Big downside is it requires that full data store in RAM. [sent-103, score-0.417]

15 Sequential reads are fast because data in a column is stored together. [sent-134, score-0.478]

16 When using column database you are almost always scanning the entire column. [sent-137, score-0.422]

17 In exchange for losing data you can store all information in constant space Gives you false positives at a known error rate. [sent-147, score-0.358]

18 Knowing a bloom filter you can locally perform a computation to know which nodes the data may be on. [sent-150, score-0.52]

19 LucidDB Column Database Java/C++ open source data warehouse No clustering so only single node performance, but that can be enough for the applications column stores are good at. [sent-155, score-0.655]

20 Vertica - blazing-fast data warehousing software LucidDB - the first and only open-source RDBMS purpose-built entirely for data warehousing and business intelligence. [sent-161, score-0.432]


similar blogs computed by tfidf model

tfidf for this blog:

wordName wordTfidf (topN-words)

[('column', 0.345), ('acid', 0.252), ('store', 0.225), ('compression', 0.185), ('nodes', 0.159), ('consistent', 0.149), ('write', 0.139), ('bloom', 0.136), ('data', 0.133), ('har', 0.115), ('bob', 0.113), ('row', 0.104), ('stores', 0.097), ('filters', 0.095), ('tag', 0.094), ('values', 0.092), ('filter', 0.092), ('client', 0.092), ('document', 0.091), ('hashing', 0.087), ('warehousing', 0.083), ('bigtable', 0.082), ('io', 0.08), ('node', 0.08), ('rebalancing', 0.08), ('correct', 0.077), ('database', 0.077), ('cache', 0.072), ('hash', 0.071), ('keys', 0.069), ('clocks', 0.066), ('project', 0.066), ('reduces', 0.065), ('presentation', 0.064), ('view', 0.064), ('attributes', 0.063), ('indexes', 0.062), ('transaction', 0.061), ('downside', 0.059), ('lots', 0.059), ('products', 0.059), ('queries', 0.058), ('maps', 0.057), ('master', 0.057), ('handle', 0.057), ('written', 0.056), ('distributed', 0.056), ('talk', 0.055), ('builds', 0.054), ('characteristics', 0.054)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 0.9999997 589 high scalability-2009-05-05-Drop ACID and Think About Data

Introduction: The abstract for the talk given by Bob Ippolito, co-founder and CTO of Mochi Media, Inc: Building large systems on top of a traditional single-master RDBMS data storage layer is no longer good enough. This talk explores the landscape of new technologies available today to augment your data layer to improve performance and reliability. Is your application a good fit for caches, bloom filters, bitmap indexes, column stores, distributed key/value stores, or document databases? Learn how they work (in theory and practice) and decide for yourself. Bob does an excellent job highlighting different products and the key concepts to understand when pondering the wide variety of new database offerings. It's unlikely you'll be able to say oh, this is the database for me after watching the presentation, but you will be much better informed on your options. And I imagine slightly confused as to what to do :-) An interesting observation in the talk is that the more robust products are internal

2 0.38211784 658 high scalability-2009-07-17-Against all the odds

Introduction: This article not about Mariah Carey, or its song. It's about Storing System, Database. First let's describe what means by odds: In my social network, I found 93% of the mainstream developers sanctify the database, or at least consider it in any data persistence challenge as the ultimate, superhero, and undefeatable solution. I think this problem come from the education, personally, and some companies also I think it's involved in this. To start to fix this bad thinking, we all should agree in the following points: Every challenge have its own solutions, so whatever you want to save/persistent, there are always many solutions. For example the Web search engines, such as: Google, Kngine, Yahoo, Bing don't use database at all instead we use Indexes (Index file) for better performance. The Database in general whatever the vendor it's slow compared with other solutions such as: Key-Value storing system, Index file, DHT. The Database currently employ Relation Data model

3 0.22103155 448 high scalability-2008-11-22-Google Architecture

Introduction: Update 2: Sorting 1 PB with MapReduce . PB is not peanut-butter-and-jelly misspelled. It's 1 petabyte or 1000 terabytes or 1,000,000 gigabytes. It took six hours and two minutes to sort 1PB (10 trillion 100-byte records) on 4,000 computers and the results were replicated thrice on 48,000 disks. Update: Greg Linden points to a new Google article MapReduce: simplified data processing on large clusters . Some interesting stats: 100k MapReduce jobs are executed each day; more than 20 petabytes of data are processed per day; more than 10k MapReduce programs have been implemented; machines are dual processor with gigabit ethernet and 4-8 GB of memory. Google is the King of scalability. Everyone knows Google for their large, sophisticated, and fast searching, but they don't just shine in search. Their platform approach to building scalable applications allows them to roll out internet scale applications at an alarmingly high competition crushing rate. Their goal is always to build

4 0.21515876 1064 high scalability-2011-06-20-35+ Use Cases for Choosing Your Next NoSQL Database

Introduction: We've asked  What The Heck Are You Actually Using NoSQL For? . We've asked  101 Questions To Ask When Considering A NoSQL Database . We've even had a  webinar   What Should I Do? Choosing SQL, NoSQL or Both for Scalable Web Applications . Now we get to the point of considering use cases and which systems might be appropriate for those use cases. What are your options? First, let's cover what are the various data models. These have been adapted from Emil Eifrem and NoSQL databases .  Document Databases Lineage: Inspired by Lotus Notes. Data model: Collections of documents, which contain key-value collections. Example: CouchDB, MongoDB  Good at: Natural data modeling. Programmer friendly. Rapid development. Web friendly, CRUD. Graph Databases Lineage: Euler and graph theory. Data model: Nodes & relationships, both which can hold key-value pairs Example: AllegroGraph, InfoGrid, Neo4j Good at:  Rock complicated graph problems. Fast.

5 0.21220073 954 high scalability-2010-12-06-What the heck are you actually using NoSQL for?

Introduction: It's a truism that we should choose the right tool for the job . Everyone says that. And who can disagree? The problem is this is not helpful advice without being able to answer more specific questions like: What jobs are the tools good at? Will they work on jobs like mine? Is it worth the risk to try something new when all my people know something else and we have a deadline to meet? How can I make all the tools work together? In the NoSQL space this kind of real-world data is still a bit vague. When asked, vendors tend to give very general answers like NoSQL is good for BigData or key-value access. What does that mean for for the developer in the trenches faced with the task of solving a specific problem and there are a dozen confusing choices and no obvious winner? Not a lot. It's often hard to take that next step and imagine how their specific problems could be solved in a way that's worth taking the trouble and risk. Let's change that. What problems are you using NoSQL to sol

6 0.20627299 920 high scalability-2010-10-15-Troubles with Sharding - What can we learn from the Foursquare Incident?

7 0.20474012 327 high scalability-2008-05-27-How I Learned to Stop Worrying and Love Using a Lot of Disk Space to Scale

8 0.20329665 538 high scalability-2009-03-16-Are Cloud Based Memory Architectures the Next Big Thing?

9 0.19721203 890 high scalability-2010-09-01-Paper: The Case for Determinism in Database Systems

10 0.17414428 849 high scalability-2010-06-28-VoltDB Decapitates Six SQL Urban Myths and Delivers Internet Scale OLTP in the Process

11 0.17408593 1514 high scalability-2013-09-09-Need Help with Database Scalability? Understand I-O

12 0.17341527 342 high scalability-2008-06-08-Search fast in million rows

13 0.17126949 1008 high scalability-2011-03-22-Facebook's New Realtime Analytics System: HBase to Process 20 Billion Events Per Day

14 0.16733629 578 high scalability-2009-04-23-Which Key value pair database to be used

15 0.16717348 651 high scalability-2009-07-02-Product: Project Voldemort - A Distributed Database

16 0.16569948 280 high scalability-2008-03-17-Paper: Consistent Hashing and Random Trees: Distributed Caching Protocols for Relieving Hot Spots on the World Wide Web

17 0.16373545 1440 high scalability-2013-04-15-Scaling Pinterest - From 0 to 10s of Billions of Page Views a Month in Two Years

18 0.16096719 1062 high scalability-2011-06-15-101 Questions to Ask When Considering a NoSQL Database

19 0.15988156 1508 high scalability-2013-08-28-Sean Hull's 20 Biggest Bottlenecks that Reduce and Slow Down Scalability

20 0.15976807 666 high scalability-2009-07-30-Learn How to Think at Scale


similar blogs computed by lsi model

lsi for this blog:

topicId topicWeight

[(0, 0.303), (1, 0.186), (2, -0.033), (3, 0.024), (4, 0.054), (5, 0.239), (6, 0.023), (7, -0.049), (8, 0.008), (9, -0.022), (10, 0.063), (11, 0.019), (12, -0.112), (13, -0.019), (14, 0.066), (15, 0.038), (16, -0.037), (17, -0.026), (18, -0.0), (19, -0.073), (20, 0.006), (21, 0.052), (22, 0.071), (23, 0.058), (24, -0.082), (25, -0.082), (26, 0.095), (27, 0.057), (28, -0.017), (29, -0.017), (30, -0.051), (31, -0.011), (32, -0.014), (33, -0.004), (34, 0.001), (35, -0.008), (36, -0.017), (37, 0.03), (38, -0.032), (39, -0.054), (40, -0.023), (41, -0.007), (42, 0.044), (43, 0.039), (44, -0.024), (45, 0.038), (46, 0.036), (47, -0.017), (48, 0.043), (49, 0.083)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 0.96127337 589 high scalability-2009-05-05-Drop ACID and Think About Data

Introduction: The abstract for the talk given by Bob Ippolito, co-founder and CTO of Mochi Media, Inc: Building large systems on top of a traditional single-master RDBMS data storage layer is no longer good enough. This talk explores the landscape of new technologies available today to augment your data layer to improve performance and reliability. Is your application a good fit for caches, bloom filters, bitmap indexes, column stores, distributed key/value stores, or document databases? Learn how they work (in theory and practice) and decide for yourself. Bob does an excellent job highlighting different products and the key concepts to understand when pondering the wide variety of new database offerings. It's unlikely you'll be able to say oh, this is the database for me after watching the presentation, but you will be much better informed on your options. And I imagine slightly confused as to what to do :-) An interesting observation in the talk is that the more robust products are internal

2 0.84482408 658 high scalability-2009-07-17-Against all the odds

Introduction: This article not about Mariah Carey, or its song. It's about Storing System, Database. First let's describe what means by odds: In my social network, I found 93% of the mainstream developers sanctify the database, or at least consider it in any data persistence challenge as the ultimate, superhero, and undefeatable solution. I think this problem come from the education, personally, and some companies also I think it's involved in this. To start to fix this bad thinking, we all should agree in the following points: Every challenge have its own solutions, so whatever you want to save/persistent, there are always many solutions. For example the Web search engines, such as: Google, Kngine, Yahoo, Bing don't use database at all instead we use Indexes (Index file) for better performance. The Database in general whatever the vendor it's slow compared with other solutions such as: Key-Value storing system, Index file, DHT. The Database currently employ Relation Data model

3 0.83842301 676 high scalability-2009-08-08-Yahoo!'s PNUTS Database: Too Hot, Too Cold or Just Right?

Introduction: So far every massively scalable database is a bundle of compromises. For some the weak guarantees of Amazon's eventual consistency model are too cold. For many the strong guarantees of standard RDBMS distributed transactions are too hot. Google App Engine tries to get it just right with entity groups . Yahoo! is also trying to get is just right by offering per-record timeline consistency, which hopes to serve up a heaping bowl of rich database functionality and low latency at massive scale : We describe PNUTS [Platform for Nimble Universal Table Storage], a massively parallel and geographically distributed database system for Yahoo!’s web applications. PNUTS provides data storage organized as hashed or ordered tables, low latency for large numbers of con-current requests including updates and queries, and novel per-record consistency guarantees. It is a hosted, centrally managed, and geographically distributed service, and utilizes automated load-balancing and failover to redu

4 0.83774823 651 high scalability-2009-07-02-Product: Project Voldemort - A Distributed Database

Introduction: Update: Presentation from the NoSQL conference : slides , video 1 , video 2 . Project Voldemort is an open source implementation of the basic parts of Dynamo (Amazon’s Highly Available Key-value Store) distributed key-value storage system. LinkedIn is using it in their production environment for "certain high-scalability storage problems where simple functional partitioning is not sufficient." From their website: Data is automatically replicated over multiple servers. Data is automatically partitioned so each server contains only a subset of the total data Server failure is handled transparently Pluggable serialization is supported to allow rich keys and values including lists and tuples with named fields, as well as to integrate with common serialization frameworks like Protocol Buffers, Thrift, and Java Serialization Data items are versioned to maximize data integrity in failure scenarios without compromising availability of the system Each node is independent o

5 0.82498288 1017 high scalability-2011-04-06-Netflix: Run Consistency Checkers All the time to Fixup Transactions

Introduction: You might have consistency problems if you have: multiple datastores in multiple datacenters, without distributed transactions, and with the ability to alternately execute out of each datacenter;  syncing protocols that can fail or sync stale data; distributed clients that cache data and then write old back to the central store; a NoSQL database that doesn't have transactions between updates of multiple related key-value records; application level integrity checks; client driven optimistic locking . Sounds a lot like many evolving, loosely coupled, autonomous, distributed systems these days. How do you solve these consistency problems? Siddharth "Sid" Anand of Netflix talks about how they solved theirs in his excellent presentation,  NoSQL @ Netflix : Part 1 , given to a packed crowd at a Cloud Computing Meetup .  You might be inclined to say how silly it is to have these problems in the first place, but just hold on. See if you might share some of their problems, before gettin

6 0.81414962 705 high scalability-2009-09-16-Paper: A practical scalable distributed B-tree

7 0.80690563 756 high scalability-2009-12-30-Terrastore - Scalable, elastic, consistent document store.

8 0.78499854 1304 high scalability-2012-08-14-MemSQL Architecture - The Fast (MVCC, InMem, LockFree, CodeGen) and Familiar (SQL)

9 0.77625084 468 high scalability-2008-12-17-Ringo - Distributed key-value storage for immutable data

10 0.7759912 799 high scalability-2010-03-23-Digg: 4000% Performance Increase by Sorting in PHP Rather than MySQL

11 0.77534318 1092 high scalability-2011-08-04-Jim Starkey is Creating a Brave New World by Rethinking Databases for the Cloud

12 0.77311826 1463 high scalability-2013-05-23-Paper: Calvin: Fast Distributed Transactions for Partitioned Database Systems

13 0.77135223 1345 high scalability-2012-10-22-Spanner - It's About Programmers Building Apps Using SQL Semantics at NoSQL Scale

14 0.76586646 971 high scalability-2011-01-10-Riak's Bitcask - A Log-Structured Hash Table for Fast Key-Value Data

15 0.76198423 50 high scalability-2007-07-31-BerkeleyDB & other distributed high performance key-value databases

16 0.76134574 1096 high scalability-2011-08-10-LevelDB - Fast and Lightweight Key-Value Database From the Authors of MapReduce and BigTable

17 0.75452727 1236 high scalability-2012-04-30-Masstree - Much Faster than MongoDB, VoltDB, Redis, and Competitive with Memcached

18 0.75054854 784 high scalability-2010-02-25-Paper: High Performance Scalable Data Stores

19 0.74669248 1205 high scalability-2012-03-07-Scale Indefinitely on S3 With These Secrets of the S3 Masters

20 0.74653083 849 high scalability-2010-06-28-VoltDB Decapitates Six SQL Urban Myths and Delivers Internet Scale OLTP in the Process


similar blogs computed by lda model

lda for this blog:

topicId topicWeight

[(0, 0.02), (1, 0.132), (2, 0.242), (10, 0.049), (30, 0.041), (61, 0.115), (68, 0.042), (77, 0.017), (79, 0.14), (85, 0.046), (94, 0.053)]

similar blogs list:

simIndex simValue blogId blogTitle

1 0.98185706 849 high scalability-2010-06-28-VoltDB Decapitates Six SQL Urban Myths and Delivers Internet Scale OLTP in the Process

Introduction: What do you get when you take a SQL database and start a new implementation from scratch, taking advantage of the latest research and modern hardware?  Mike Stonebraker , the sword wielding Johnny Appleseed of the database world, hopes you get something like his new database, VoltDB : a pure SQL, pure ACID, pure OLTP, shared nothing, sharded, scalable, lockless, open source, in-memory DBMS, purpose-built for running hundreds of thousands of transactions a second. VoltDB claims to be 100 times faster than MySQL, up to 13 times faster than Cassandra , and 45 times faster than Oracle, with near-linear scaling. Will VoltDB kill off the new NoSQL upstarts? Will VoltDB cause a mass extinction of ancient databases? Probably no and no to both questions, but it's a product with a definite point-of-view and is worth a look as the transaction component in your system. But will it be right for you? Let's see... I first heard the details about VoltDB at  Gluecon , where Mr. Stonebraker pres

2 0.98160154 1112 high scalability-2011-09-07-What Google App Engine Price Changes Say About the Future of Web Architecture

Introduction: When I was a child, I spake as a child, I understood as a child, I thought as a child: but when I became a man, I put away childish things . -- Corinthians With this new pricing, developments will be driven by the costs . I like to optimize my apps to make them better or faster, but to optimize them just to make them cheaper is a waste of time. -- Sylvain on Google Groups The dream is dead. Google App Engine's bold pay for what you use dream dies as it leaves childish things behind and becomes a real product . Pricing will change . Architectures will change. Customers will change. Hearts and minds will change. But Google App Engine  will survive.  Google is shutting down many of its projects . GAE is not among them. Do we have GAE's pricing change to thank for it surving the  more wood behind more deadly arrows push? Without a radical and quick shift towards profitably GAE would no doubt be a historical footnote in the long scroll of good ideas. The urgency involve

same-blog 3 0.9797098 589 high scalability-2009-05-05-Drop ACID and Think About Data

Introduction: The abstract for the talk given by Bob Ippolito, co-founder and CTO of Mochi Media, Inc: Building large systems on top of a traditional single-master RDBMS data storage layer is no longer good enough. This talk explores the landscape of new technologies available today to augment your data layer to improve performance and reliability. Is your application a good fit for caches, bloom filters, bitmap indexes, column stores, distributed key/value stores, or document databases? Learn how they work (in theory and practice) and decide for yourself. Bob does an excellent job highlighting different products and the key concepts to understand when pondering the wide variety of new database offerings. It's unlikely you'll be able to say oh, this is the database for me after watching the presentation, but you will be much better informed on your options. And I imagine slightly confused as to what to do :-) An interesting observation in the talk is that the more robust products are internal

4 0.97936362 1206 high scalability-2012-03-09-Stuff The Internet Says On Scalability For March 9, 2012

Introduction: You've Got Questions We've Got HighScalability: 1 trillion bits per second : IBM’s Holey Optochip; Scale of the Universe : 2; Infinite wireless : Vortex radio waves;  105,000 Servers : Akamai. Quotable quotes: @CodingFabian : IaaS = Ops without Hardware; PaaS = Devs without Ops; SaaS = Business without Devs @audaciouslife : While I was away 90K signed on MT @akfirat One course, 90K students. Talk about scalability in education  @dthume : "Fault tolerance implies scalability" - Joe Armstrong,  @jessiekeck : Looks like my local bar takes the same approach to scalability with their paper towels as I do w/ software. http://pic.twitter.com/DTL2W1eC @neil_conway : Weird: network locality is no longer important within a DC and yet communication predicted to dominate computation cost in manycore CPUs @coda : You don't "beat the CAP theorem". You "build distributed systems that don't suck miserably". At best. @drunkcod : "programme

5 0.97908396 1534 high scalability-2013-10-18-Stuff The Internet Says On Scalability For October 18th, 2013

Introduction: Hey, it's HighScalability time: Test your sense of scale. Is this image of something microscopic or macroscopic? Find out . $3.5 million : Per Episode Cost of Breaking Bad Quotable Quotes: @GammaCounter : "There are 400 billion trees in the Amazon River basin, close to the number of stars in the Milky Way galaxy."  @rbranson : Virtualization has near-zero overhead, unless the VM spends most of it's time copying between RAM and network… like memcached or haproxy. @HackerNewsOnion : Programming is 1% inspiration, 99% trying to get your environment working. @aneel : "roundtrips, not bandwidth, is now often the bottleneck for most applications" @jamesurquhart : Not to mention the fact that auto-scaling should happen above IaaS layer. Think multi-cloud. Sheref Mansy : A machine keeps sort of chugging away, without worrying about its environment. But a living system has to. V.D. Veksler : it just came to my attention that Javascri

6 0.97867268 1460 high scalability-2013-05-17-Stuff The Internet Says On Scalability For May 17, 2013

7 0.97855121 1600 high scalability-2014-02-21-Stuff The Internet Says On Scalability For February 21st, 2014

8 0.978104 1020 high scalability-2011-04-12-Caching and Processing 2TB Mozilla Crash Reports in memory with Hazelcast

9 0.97781384 1649 high scalability-2014-05-16-Stuff The Internet Says On Scalability For May 16th, 2014

10 0.97768319 1428 high scalability-2013-03-22-Stuff The Internet Says On Scalability For March 22, 2013

11 0.97684968 517 high scalability-2009-02-21-Google AppEngine - A Second Look

12 0.97653419 1509 high scalability-2013-08-30-Stuff The Internet Says On Scalability For August 30, 2013

13 0.97633016 1382 high scalability-2013-01-07-Analyzing billions of credit card transactions and serving low-latency insights in the cloud

14 0.97615474 1431 high scalability-2013-03-29-Stuff The Internet Says On Scalability For March 29, 2013

15 0.97569215 1037 high scalability-2011-05-10-Viddler Architecture - 7 Million Embeds a Day and 1500 Req-Sec Peak

16 0.97516036 1389 high scalability-2013-01-18-Stuff The Internet Says On Scalability For January 18, 2013

17 0.9751544 1256 high scalability-2012-06-04-OpenFlow-SDN is Not a Silver Bullet for Network Scalability

18 0.97513402 1148 high scalability-2011-11-29-DataSift Architecture: Realtime Datamining at 120,000 Tweets Per Second

19 0.97507602 301 high scalability-2008-04-08-Google AppEngine - A First Look

20 0.97503287 195 high scalability-2007-12-28-Amazon's EC2: Pay as You Grow Could Cut Your Costs in Half