[SSA] 03.newsql database (2014.02.05)

[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

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.


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.


19

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

20

The evolving database landscape

OSBC

21

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


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


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

 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

 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 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

(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

43

 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


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


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.

46

Google Spanner - True Time

TT.now()

earliest

time
latest

2*ε

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.


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


49

Google Spanner - Evaluation (2/2)

Effect of killing servers on throughput


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


53

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

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.


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.


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.


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


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

 Hypertable Couchbase Membrain MongoDB Redis
 Data grid/cache
 VoltDB, Clustrix

 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

 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

 MongoDB, CouchDB, Cassandra
 Google App Engine, SimpleDB, SQL Azure

 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

 Neo4j GraphDB, InfiniteGraph
 Apache Cassandra, Hadoop,
 VoltDB, Clustrix

 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

 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

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[SSA] 03.newsql database (2014.02.05)

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