[SSA] 03.newsql database (2014.02.05)
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Transcript of [SSA] 03.newsql database (2014.02.05)
1
I. Why NewSQL?
II. NewSQL 기본 개념
III. NewSQL 종류
IV.NewSQL 정리
Contents
2
Why NewSQL?
3
Thinking – Extreme Data
Qcon London 2012
4 출처 : Netflix in the Cloud (http://www.slideshare.net/adrianco/netflix-in-the-cloud-2011)
Thinking - Traffic Explosion
5 Qcon London 2012
Organizations need deeper insights
6
Solutions
□Buy High end Technology
□Higher more developers
□Using NoSQL
□Using NewSQL
7
Solution – Buy High End Technology
Oracle, IBM
8
Solution – Higher more developers
http://www.trekbikes.com/us/en/bikes/road/race_performance/madone_4_series/madone_4_5
□Application Level Sharding
□Build your replication middleware
□…
9
Solutions – Use NoSQL
□새로운 비 관계형 데이터 베이스
□분산 아키텍처
□수평 확장성
□고정된 테이블 스키마가 없음
□Join, UPDATE, DELETE 연산이 없음
□트랜잭션이 없음
□SQL 지원이 없음
10
NoSQL Ecosystems
451 group
11
MongoDB
□Document-oriented database JSON-style documents: Lists, Maps, primitives
Schema-less
□Transaction = update of a single document
□Rich query language for dynamic queries
□Tunable writes: speed reliability
□Highly scalable and available
12
MongoDB 사용예
□Use cases High volume writes
Complex data
Semi-structured data
□주요 고객 Foursquare
Bit.ly Intuit
SourceForge, NY Times
GILT Groupe, Evite,
SugarCRM
13
Apache Cassandra
□Column-oriented database/Extensible row store Think Row ~= java.util.SortedMap
□Transaction = update of a row
□Fast writes = append to a log
□Tunable reads/writes: consistency / availability
□Extremely scalable
Transparent and dynamic clustering
Rack and datacenter aware data replication
□CQL = “SQL”-like DDL and DML
14
Apache Cassandra 사용 예
□사용 예 Big data
Multiple Data Center distributed database
Persistent cache
(Write intensive) Logging
High-availability (writes)
□주요 고객 Digg, Facebook, Twitter, Reddit, Rackspace
Cloudkick, Cisco, SimpleGeo, Ooyala, OpenX
The largest production cluster has over 100 TB of data in over 150 machines.“ – Casssandra web site
15
□새로운 관계형 데이터베이스
□SQL과 ACID 트랜잭션을 유지
□새롭고 개선된 분산 아키텍처
□뛰어난 확장성과 성능을 지원
□NewSQL vendors: TokuDB, ScaleDB, NimbusDB, ..., VoltDB
Solutions – Use NewSQL
16 http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf
17
NewSQL 정의 – Wikipedia
NewSQL is a class of modern relational
database management systems that seek to
provide the same scalable performance of
NoSQL systems for OLTP workloads while still
maintaining the ACID guarantees of a
traditional single-node database system
NewSQL is a class of modern relational
database management systems that seek to
provide the same scalable performance of
NoSQL systems for OLTP workloads while still
maintaining the ACID guarantees of a
traditional single-node database system
http://en.wikipedia.org/wiki/NewSQL
18
NewSQL 정의 – 451 Group
A DBMS that delivers the scalability and
flexibility promised by NoSQL while retaining
the support for SQL queries and/or ACID, or
to improve performance for appropriate
workloads.
A DBMS that delivers the scalability and
flexibility promised by NoSQL while retaining
the support for SQL queries and/or ACID, or
to improve performance for appropriate
workloads.
http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf
19
NewSQL 정의 – Stonbraker
SQL as the primary interface.
ACID support for transactions
Non-locking concurrency control.
High per-node performance.
Parallel, shared-nothing architecture.
SQL as the primary interface.
ACID support for transactions
Non-locking concurrency control.
High per-node performance.
Parallel, shared-nothing architecture.
http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf
20
NewSQL Category New Database New MySQL Storage Engines Transparent Clustering
21 OSBC
The evolving database landscape
22
MySQL Ecosystem
23
NewSQL Ecosystem
24
New Database
□ Newly designed from scratch to achieve scalability and performance One of the key considerations in improving the performance is
making non-disk (memory) or new kinds of disks (flash/SSD) the primary data store.
some (hopefully minor) changes to the code will be required and data migration is still needed.
□Solutions Software-Only: VoltDB, NuoDB, Drizzle, Google Spanner
Supported as an appliance: Clustrix, Translattice.
http://www.linuxforu.com/2012/01/newsql-handle-big-data/
25
New MySQL Storage Engines
□Highly optimized storage engines for MySQL
□Scale better than built-in engines, such as InnoDB. Good part: the usage of the MySQL interface
Downside part: data migration from other databases
□Solutions TokuDB, MemSQL, Xeround, Akiban, NDB
http://www.linuxforu.com/2012/01/newsql-handle-big-data/
26
Transparent Clustering
□Retain the OLTP databases in their original format, but provide a pluggable feature Cluster transparently
Ensure Scalability
□Avoid the rewrite code or perform any data migration
□Solutions Cluster transparently: Schooner MySQL, Continuent
Tungsten, ScalArc
Ensure Scalability: ScaleBase, dbShards
http://www.linuxforu.com/2012/01/newsql-handle-big-data/
27
NewSQL Products VoltDB Google Spanner MySQL Cluster Architecture
28
29
VoltDB
http://voltdb.com/products-services/products, http://www.slideshare.net/chris.e.richardson/polygot-persistenceforjavadevs-jfokus2012reorgpptx
□VoltDB, 2010, AGPLv3/VoltDB Proprietary License, Java/C++ □Type: NewSQL, New Database □Main Point: In-memory Database, Java Stored Procedure, VoltDB
implements the design of the academic H-Store project □Protocol: SQL □Transaction: Yes □Data Storage: Memory
□Features
□ in-memory relational database □Durability thru replication, snapshots, logging □Transparent partitioning □ACID-level consistency □Synchronous multi-master replication □Database Replication
30
VoltDB- Technical Overview
“OLTP Through the Looking Glass”
VoltDB는 전통 DB의 오버헤드 회피함 – 데이터 및 관련 프로세싱은 함께 분할 되고, CPU 코어
단위로 분산됨
Shared-Nothing Architecture Horizontally Scale
– 데이터는 메인 메모리에서 유지됨
버퍼관리가 필요 없음
– Transaction은 메모리에서 순차적으로 실행됨
no locking & latching
– Synchronous multi-master replication: HA
– Command Logging: “write-ahead” 데이터 로깅을 대체하여 고성능을 제공
출처: http://odbms.org/download/VoltDBTechnicalOverview.pdf, http://cs-www.cs.yale.edu/homes/dna/papers/oltpperf-sigmod08.pdf
31
X X
X
X X
VoltDB - Partitions (1/3)
1 partition per physical CPU core –Each physical server has multiple VoltDB partitions
Data - Two types of tables –Partitioned
Single column serves as partitioning key Rows are spread across all VoltDB partitions by partition column Transactional data (high frequency of modification)
–Replicated All rows exist within all VoltDB partitions Relatively static data (low frequency of
modification)
Code - Two types of work – both ACID –Single-Partition
All insert/update/delete operations within single partition Majority of transactional workload
–Multi-Partition CRUD against partitioned tables across multiple partitions Insert/update/delete on replicated tables
출처: http://strataconf.com/stratany2013/public/schedule/detail/31731
32
VoltDB - Partitions (2/3)
Single-partition vs. Multi-partition
1 101 2
1 101 3
4 401 2
1 knife
2 spoon
3 fork
Partition 1
2 201 1
5 501 3
5 502 2
1 knife
2 spoon
3 fork
Partition 2
3 201 1
6 601 1
6 601 2
1 knife
2 spoon
3 fork
Partition 3
table orders : customer_id (partition key) (partitioned) order_id product_id
table products : product_id (replicated) product_name
select count(*) from orders where customer_id = 5 single-partition
select count(*) from orders where product_id = 3 multi-partition
insert into orders (customer_id, order_id, product_id) values (3,303,2) single-partition
update products set product_name = ‘spork’ where product_id = 3 multi-partition
33
VoltDB - Partitions (3/3)
Looking inside a VoltDB partition… – Each partition contains data and an
execution engine.
– The execution engine contains a queue for transaction requests.
– Requests are executed sequentially (single threaded).
Work
Queue
execution engine
Table Data Index Data
- Complete copy of all replicated tables - Portion of rows (about 1/partitions) of all partitioned tables
34
VoltDB - Compiling
The database is constructed from – The schema (DDL)
– The work load (Java stored procedures)
– The Project (users, groups, partitioning)
VoltCompiler creates application catalog – Copy to servers along with 1 .jar and
1 .so
– Start servers
CREATE TABLE HELLOWORLD (
HELLO CHAR(15),
WORLD CHAR(15),
DIALECT CHAR(15),
PRIMARY KEY (DIALECT)
);
Schema
import org.voltdb. * ;
@ProcInfo(
partitionInfo = "HELLOWORLD.DIA
singlePartition = true
)
public class Insert extends VoltPr
public final SQLStmt sql =
new SQLStmt("INSERT INTO HELLO
public VoltTable[] run( String hel
import org.voltdb. * ;
@ProcInfo(
partitionInfo = "HELLOWORLD.DIA
singlePartition = true
)
public class Insert extends VoltPr
public final SQLStmt sql =
new SQLStmt("INSERT INTO HELLO
public VoltTable[] run( String hel
import org.voltdb. * ;
@ProcInfo(
partitionInfo = "HE
singlePartition = t
public final SQLStmt
public VoltTable[] run
Stored Procedures
<?xml version="1.0"?>
<project>
<database name='data
<schema path='ddl.
<partition table=‘
</database>
</project>
Project.xml
35
VoltDB - Transactions
All access to VoltDB is via Java stored procedures (Java + SQL)
A single invocation of a stored procedure is a transaction (committed on success)
Limits round trips between DBMS and application
High performance client applications communicate synchronously with VoltDB
SQL
36
VoltDB - Clusters/Durability
Scalability – Increase RAM in servers to add capacity
– Add servers to increase performance / capacity
– Consistently measuring 90% of single-node performance increase per additional node
High availability – K-safety for redundancy
Snapshots – Scheduled, continuous, on demand
Spooling to data warehouse
Disaster Recovery/WAN replication (Future) – Asynchronous replication
37
Google Spanner
38 출처: http://research.google.com/archive/spanner.html
Google Spanner Overview
구글의 확장성, 다중버전, 전 세계적으로 분산, 동기적으로 복제된 데이터베이스
Google, 2012, Paper, C++
Type: NewSQL, New Database
Main Point: Distributed multiversion database
특징
– General-purpose transactions (ACID)
– SQL query language
– Schematized tables
– Semi-relational data model
Running in production
– Storage for Google’s ad data(F1)
– Replaced a sharded MySQL database
39
Google Spanner - Key Features
Temporal Multi-version database
Externally consistent global write-transactions with synchronous replication.
Transactions across Datacenters.
Lock-free read-only transactions.
Schematized, semi-relational (tabular) data model.
SQL-like query interface.
Auto-sharding, auto-rebalancing, automatic failure response.
Exposes control of data replication and placement to user/application.
Enables transaction serialization via global timestamps
Acknowledges clock uncertainty and guarantees a bound on it
Uses novel TrueTime API to accomplish concurrency control
Uses GPS devices and Atomic clocks to get accurate time
출처: http://www.cse.buffalo.edu/~okennedy/courses/cse704fa2012/9.2-Spanner.ppt
40 출처: http://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf
Google Spanner - Design Goals
41
Google Spanner - Architecture
• Universe: Spanner 전체 집합 • Zone: 물리적으로 독립되어 운영할 수 있는 단위 • 1대의 존마스터(zonemaster)+ 수백~수천 대의 스팬서버(spanserver)로 구성됨 • 존마스터는 스팬서버에 데이터를 할당, 스팬서버는 실제 데이터를 저장하고 처리 • 로케이션 프록시: 클라이언트에 의해 호출되어, 접근해야 할 데이터가 어느 스팬서버에 있는지 알려줌
출처: http://helloworld.naver.com/helloworld/216593
42
Google Spanner - Software Stack (1/3)
• 스팬서버는 100~1000개의 태블릿(tablet)을 관리. • 태블릿: ‘(key:string, timestamp:int64) string’ 형태의 매핑을 다수개 저장, 멀티다중 버전 데이터베이스 특성 • 태블릿의 상태는 B-트리 파일과 WAL로 CFS에 저장됨 • 스팬서버간 데이터 복제를 지원하기 위해 팍소스 스테이트 머신을 이용
출처: http://helloworld.naver.com/helloworld/216593
43
Google Spanner - Software Stack (2/3)
(key:string, timestamp:int64) → string
Back End: Colossus (successor to GFS)
Paxos State Machine on top of each tablet stores meta data and logs of the tablet.
Leader among replicas in a Paxos group is chosen and all write requests for replicas in that group initiate at leader.
Transaction Leader
– Is Paxos Leader if transaction involves one Paxos group
출처: http://www.cse.buffalo.edu/~okennedy/courses/cse704fa2012/9.2-Spanner.ppt
44
Google Spanner - Software Stack (3/3)
Directory – 같은 접두어를 사용하는 연속된 키 모음
– 빅테이블(BigTable)의 버킷과 유사
– 데이터 배치의 최소 단위
– 지리적인 리플리카 배치의 최소단위
디렉터리의 크기가 너무 커질 경우, 하나의 디렉터리를 여러 개의 프래그먼트(fragment)로 분할 가능
출처: http://www.cse.buffalo.edu/~okennedy/courses/cse704fa2012/9.2-Spanner.ppt
45
Google Spanner - Data Model
One or more databases supported in Spanner Universe
Database can contain unlimited schematized tables
Not purely relational
– Requires rows to have names
– Names are nothing but a set(can be singleton) of primary keys
– In a way, it’s a key value store with primary keys mapped to non-key columns as values
출처: http://www.cse.buffalo.edu/~okennedy/courses/cse704fa2012/9.2-Spanner.ppt
46
Google Spanner - TrueTime
“Global wall-clock time” with bounded uncertainty
Novel API behind Spanner’s core innovation
Leverages hardware features like GPS and Atomic Clocks
Implemented via TrueTime API.
–Key method being now() which not only returns current system time but also another value (ε) which tells the maximum uncertainty in the time returned
Set of time master server per datacenters and time slave daemon per machines.
Majority of time masters are GPS fitted and few others are atomic clock fitted (Armageddon masters).
Daemon polls variety of masters and reaches a consensus about correct timestamp.
출처: http://www.cse.buffalo.edu/~okennedy/courses/cse704fa2012/9.2-Spanner.ppt
47
Google Spanner - True Time
time
earliest latest
TT.now()
2*ε
출처: http://www.cse.buffalo.edu/~okennedy/courses/cse704fa2012/9.2-Spanner.ppt
48
Google Spanner - TrueTime Transaction
Read-Write – requires lock.
Read-Only – lock free.
–Requires declaration before start of transaction.
–Reads information that is up to date
Snapshot Read – Read information from past by specifying a timestamp or bound
–Use specifies specific timestamp from past or timestamp bound so that data till that point will be read.
출처: http://www.cse.buffalo.edu/~okennedy/courses/cse704fa2012/9.2-Spanner.ppt
49
Google Spanner - Evaluation(1/2)
Evaluated for replication, transactions and availability.
Results on epsilon of TrueTime
Benchmarked on Spanner System with
–50 Paxos groups
–250 Directories
–Clients(applicatons) and Zones are at a network distance of 1ms
출처: http://www.cse.buffalo.edu/~okennedy/courses/cse704fa2012/9.2-Spanner.ppt
50
Google Spanner - Evaluation (2/2)
Effect of killing servers on throughput Distribution of TrueTime Epsilon values
출처: http://www.cse.buffalo.edu/~okennedy/courses/cse704fa2012/9.2-Spanner.ppt
51
MySQL Cluster Architecture
http://dev.mysql.com/doc/refman/5.5/en/mysql-cluster-overview.html
52
Schooner MySQL Active Cluster
http://www.snia.org/sites/default/files2/SDC2011/presentations/monday/DrJohnBuschHow_ScaleUp_Scale_Out.pdf
53
dbShards Architecture
http://www.linuxforu.com/2012/01/newsql-handle-big-data/
54
NewSQL 정리
55
Database 업계의 3가지 Trends
□NoSQL 데이터베이스:
분산 아키텍처의 확장성 등의 요구 사항을 충족하며, 스키마 없는 데이터
관리 요구 사항에 부합하도록 설계됨.
□NewSQL 데이터베이스:
분산 아키텍처의 확장성 등의 요구 사항을 충족하거나 혹은 수평 확장을
필요로하지 않지만 성능을 개선은 되도록 설계됨.
□Data Grid/Cache 제품:
응용 프로그램 및 데이터베이스 성능을 높이기 위해 메모리에 데이터를
저장하도록 설계됨.
56
결론 □데이터 저장을 위한 많은 솔루션이 존재
□ Oracle, MySQL만 있다는 생각은 버려야 함 □ 먼저 시스템의 데이터 속성과 요구사항을 파악(CAP, ACID/BASE) □ 한 시스템에 여러 솔루션을 적용
소규모/복잡한 관계 데이터: RDBMS 대규모 실시간 처리 데이터: NoSQL, NewSQL 대규모 저장용 데이터: Hadoop 등
□적절한 솔루션 선택 □ 반드시 운영 중 발생할 수 있는 이슈에 대해 검증 후 도입 필요 □ 대부분의 NewSQL 솔루션은 베타 상태(섣부른 선택은 독이 될 수 있음) □ 솔루션의 프로그램 코드 수준으로 검증 필요
□NewSQL 솔루션에 대한 안정성 확보 □ 솔루션 자체의 안정성은 검증이 필요하며 현재의 DBMS 수준의 안정성은 지원하
지 않음 □ 반드시 안정적인 데이터 저장 방안 확보 후 적용 필요 □ 운영 및 개발 경험을 가진 개발자 확보 어려움 □ 요구사항에 부합되는 NewSQL 선정 필요
□처음부터 중요 시스템에 적용하기 보다는 시범 적용 필요 □ 선정된 솔루션 검증, 기술력 내재화
57
감사합니다.
58
Appendix.
59
Early – 2000s
http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf
□All the big players were heavyweight and expensive.
Oracle, DB2, Sybase, SQL Server, etc.
□Open-source databases were missing important features.
Postgres, mSQL, and MySQL.
60
Early – 2000s : eBay Architecture
http://highscalability.com/ebay-architecture
61
Early – 2000s : eBay Architecture
http://highscalability.com/ebay-architecture
Push functionality to application: Joins Referential integrity Sorting done
No distributed transactions
62
Mid– 2000s
http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf
□MySQL + InnoDB is widely adopted by new web companies:
Supported transactions, replication, recovery.
Still must use custom middleware to scale out across multiple machines.
Memcache for caching queries.
63
Mid – 2000s : Facebook Architecture
http://www.techthebest.com/2011/11/29/technology-used-in-facebook/
64
Mid – 2000s : Facebook Architecture
http://www.techthebest.com/2011/11/29/technology-used-in-facebook/
Scale out using custom middleware. Store ~75% of database in Memcache. No distributed transactions.
65
Late – 2000s
http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf
□MySQL + InnoDB is widely adopted by new web companies:
Supported transactions, replication, recovery.
Still must use custom middleware to scale out across multiple machines.
Memcache for caching queries.
66
Late – 2000s : MongoDB Architecture
http://sett.ociweb.com/sett/settAug2011.html
67
Late – 2000s : MongoDB Architecture
http://sett.ociweb.com/sett/settAug2011.html
Easy to use. Becoming more like a DBMS over time. No transactions.
68
Early – 2010s
http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf
□New DBMSs that can scale across multiple machines natively and provide ACID guarantees.
MySQL Middleware
Brand New Architectures
69
Database SPRAIN (by 451Group)
70
Database SPRAIN
□“An injury to ligaments... caused by being stretched beyond normal capacity”
□Six key drivers for NoSQL/NewSQL/DDG adoption Scalability
Performance
Relaxed consistency
Agility
Intricacy
Necessity
71
Database SPRAIN - Scalability
□Associated sub-driver: Hardware economics Scale-out across clusters of commodity servers
□Example project/service/vendor BigTable HBase Riak MongoDB Couchbase, Hadoop
Amazon RDS, Xeround, SQL Azure, NimbusDB
Data grid/cache
□Associated use case: Large-scale distributed data storage
Analysis of continuously updated data
Multi-tenant PaaS data layer
72
Database SPRAIN - Scalability
□User: StumbleUpon
□Problem: Scaling problems with recommendation engine on MySQL
□Solution: HBase Started using Apache HBase to provide real-time analytics on Su.pr
MySQL lacked the performance headroom and scale
Multiple benefits including avoiding declaring schema
Enables the data to be used for multiple applications and use cases
73
Database SPRAIN - Performance
□Associated sub-driver: MySQL limitations Inability to perform consistently at scale
□Example project/service/vendor Hypertable Couchbase Membrain MongoDB Redis
Data grid/cache
VoltDB, Clustrix
□Associated use case: Real time data processing of mixed read/write workloads
Data caching
Large-scale data ingestion
74
Database SPRAIN - Performance
□User: AOL Advertising
□Problem: Real-time data processing to support targeted advertising
□Solution: Membase Server Segmentation analysis runs in CDH, results passed into Membase
Make use of its sub-millisecond data delivery
More time for analysis as part of a 40ms targeted and response time
Also real time log and event management
75
Database SPRAIN – Relaxed Consistency
□Associated sub-driver: CAP theorem The need to relax consistency in order to maintain availability
□Example project/service/vendor: Dynamo, Voldemort, Cassandra
Amazon SimpleDB
□Associated use case: Multi-data center replication
Service availability
Non-transactional data off-load
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Database SPRAIN – Relaxed Consistency
□User: Wordnik
□Problem: MySQL too consistent –blocked access to data during inserts and
created numerous temp files to stay consistent.
□Solution: MongoDB Single word definition contains multiple data items from various
sources
MongoDB stores data as a complete document
Reduced the complexity of data storage
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Database SPRAIN – Agility
□ Associated sub-driver: Polyglot persistence Choose most appropriate storage technology for app in development
□Example project/service/vendor MongoDB, CouchDB, Cassandra
Google App Engine, SimpleDB, SQL Azure
□Associated use case: Mobile/remote device synchronization
Agile development
Data caching
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Database SPRAIN – Agility
□ User: Dimagi BHOMA (Better Health Outcomes through Mentoring and Assessments) project
□Problem: Deliver patient information to clinics despite a lack of reliable Internet
connections
□Solution: Apache CouchDB Replicates data from regional to national database
When Internet connection, and power, is available
Upload patient data from cell phones to local clinic
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Database SPRAIN – Intricacy
□ Associated sub-driver: Big data, total data Rising data volume, variety and velocity
□Example project/service/vendor Neo4j GraphDB, InfiniteGraph
Apache Cassandra, Hadoop,
VoltDB, Clustrix
□Associated use case: Social networking applications
Geo-locational applications
Configuration management database
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Database SPRAIN – Intricacy
□User: Evident Software
□Problem: Mapping infrastructure dependencies for application performance
management
□Solution: Neo4j Apache Cassandra stores performance data
Neo4j used to map the correlations between different elements
Enables users to follow relationships between resources while investigating issues
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Database SPRAIN – Necessity
□ Associated sub-driver: Open source The failure of existing suppliers to address the performance,
scalability and flexibility requirements of large-scale data processing
□ Example project/service/vendor BigTable, Dynamo, MapReduce, Memcached
Hadoop HBase, Hypertable, Cassandra, Membase
Voldemort, Riak, BigCouch
MongoDB, Redis, CouchDB, Neo4J
□Associated use case: All of the above
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Database SPRAIN – Necessity
□BigTable: Google
□Dynamo: Amazon
□Cassandra: Facebook
□HBase: Powerset
□Voldemort: LinkedIn
□Hypertable: Zvents
□Neo4j: Windh Technologies
Yahoo: Apache Hadoop and Apache HBase
Digg: Apache Cassandra
Twitter: Apache Cassandra, Apache Hadoop and FlockDB