[SSA] 03.newsql database (2014.02.05)

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Information about [SSA] 03.newsql database (2014.02.05)

Published on February 25, 2014

Author: hypermin

Source: slideshare.net

[SSA] Big Data Analytics NewSQL Database 민형기 hg.min@samsung.com 2014. 2. 5.

Contents I. Why NewSQL? II. NewSQL 기본 개념 III. NewSQL 종류 IV.NewSQL 정리 1

Why NewSQL? 2

Thinking – Extreme Data Qcon London 2012 3

Thinking - Traffic Explosion 출처 : Netflix in the Cloud (http://www.slideshare.net/adrianco/netflix-in-the-cloud-2011) 4

Organizations need deeper insights Qcon London 2012 5

Solutions □Buy High end Technology □Higher more developers □Using NoSQL □Using NewSQL 6

Solution – Buy High End Technology Oracle, IBM 7

Solution – Higher more developers □Application Level Sharding □Build your replication middleware □… http://www.trekbikes.com/us/en/bikes/road/race_performance/madone_4_series/madone_4_5 8

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 9

NoSQL Ecosystems 451 group 10

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 11

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 12

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 13

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 14

Solutions – Use NewSQL □새로운 관계형 데이터베이스 □SQL과 ACID 트랜잭션을 유지 □새롭고 개선된 분산 아키텍처 □뛰어난 확장성과 성능을 지원 □NewSQL vendors: TokuDB, ScaleDB, NimbusDB, ..., VoltDB 15

http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf 16

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 http://en.wikipedia.org/wiki/NewSQL 17

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. http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf 18

NewSQL 정의 – Stonbraker  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 19

NewSQL Category  New Database  New MySQL Storage Engines  Transparent Clustering 20

The evolving database landscape OSBC 21

MySQL Ecosystem 22

NewSQL Ecosystem 23

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/ 24

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/ 25

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/ 26

NewSQL Products  VoltDB  Google Spanner  MySQL Cluster Architecture 27


VoltDB □ 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 http://voltdb.com/products-services/products, http://www.slideshare.net/chris.e.richardson/polygot-persistenceforjavadevs-jfokus2012reorgpptx 29

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 대체 하여 고성능을 제공 출처: http://odbms.org/download/VoltDBTechnicalOverview.pdf, http://cs-www.cs.yale.edu/homes/dna/papers/oltpperf-sigmod08.pdf 30

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) X X – Replicated All rows exist within all VoltDB partitions Relatively static data (low frequency of modification)  Code - Two types of work – both ACID – Single-Partition X X X 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 31

VoltDB - Partitions (2/3)  Single-partition vs. Multi-partition 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 Partition 1 1 1 4 101 101 401 1 2 3 knife spoon fork Partition 2 2 3 2 2 5 5 201 501 502 1 2 3 knife spoon fork Partition 3 1 3 2 3 6 6 201 601 601 1 2 3 knife spoon fork 1 1 2 table orders : (partitioned) customer_id (partition key) order_id product_id table products : product_id (replicated) product_name 32

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 33

VoltDB - Compiling Stored Procedures Schema  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) ); import org.voltdb. * ; import org.voltdb. * ; import @ProcInfo( org.voltdb. * ; @ProcInfo( partitionInfo = "HELLOWORLD.DIA partitionInfo true "HE @ProcInfo( singlePartition = = partitionInfo = "HELLOWORLD.DIA )singlePartition = t singlePartition = true ) public class Insert extends VoltPr public final SQLStmt sql = public final SQLStmt public class Insert extends VoltPr new SQLStmt("INSERT INTO HELLO public VoltTable[] sql = public final SQLStmt run new SQLStmt("INSERT INTO HELLO public VoltTable[] run( String hel public VoltTable[] run( String hel Project.xml <?xml version="1.0"?> <project> <database name='data <schema path='ddl. <partition table=‘ </database> </project> 34

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) SQL  Limits round trips between DBMS and application  High performance client applications communicate synchronously with VoltDB 35

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 36

Google Spanner 37

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 출처: http://research.google.com/archive/spanner.html 38

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 39

Google Spanner - Design Goals 출처: http://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf 40

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 41

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 42

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 43

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 44

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 45

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 46

Google Spanner - True Time TT.now() earliest time latest 2*ε 출처: http://www.cse.buffalo.edu/~okennedy/courses/cse704fa2012/9.2-Spanner.ppt 47

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 48

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 49

Google Spanner - Evaluation (2/2) Effect of killing servers on throughput 출처: http://www.cse.buffalo.edu/~okennedy/courses/cse704fa2012/9.2-Spanner.ppt Distribution of TrueTime Epsilon values 50

MySQL Cluster Architecture http://dev.mysql.com/doc/refman/5.5/en/mysql-cluster-overview.html 51

Schooner MySQL Active Cluster http://www.snia.org/sites/default/files2/SDC2011/presentations/monday/DrJohnBuschHow_ScaleUp_Scale_Out.pdf 52

dbShards Architecture http://www.linuxforu.com/2012/01/newsql-handle-big-data/ 53

NewSQL 정리 54

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. 55

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 56

감사합니다. 57

Appendix. 58

Early – 2000s □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. http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf 59

Early – 2000s : eBay Architecture http://highscalability.com/ebay-architecture 60

Early – 2000s : eBay Architecture  Push functionality to application:  Joins  Referential integrity  Sorting done  No distributed transactions http://highscalability.com/ebay-architecture 61

Mid– 2000s □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. http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf 62

Mid – 2000s : Facebook Architecture http://www.techthebest.com/2011/11/29/technology-used-in-facebook/ 63

Mid – 2000s : Facebook Architecture  Scale out using custom middleware.  Store ~75% of database in Memcache.  No distributed transactions. http://www.techthebest.com/2011/11/29/technology-used-in-facebook/ 64

Late – 2000s □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. http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf 65

Late – 2000s : MongoDB Architecture http://sett.ociweb.com/sett/settAug2011.html 66

Late – 2000s : MongoDB Architecture  Easy to use.  Becoming more like a DBMS over time.  No transactions. http://sett.ociweb.com/sett/settAug2011.html 67

Early – 2010s □New DBMSs that can scale across multiple machines natively and provide ACID guarantees.  MySQL Middleware  Brand New Architectures http://www.cs.brown.edu/courses/cs227/slides/newsql/newsql-intro.pdf 68

Database SPRAIN (by 451Group) 69

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 70

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 71

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 72

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 73

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 74

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 75

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 76

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 77

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 78

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 79

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 80

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 81

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 82

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