accumulo design

APACHE ACCUMULOFrom a design perspective

SCALABLE KEY-VALUE STORE BASED ON GOOGLE'S

BIGTABLE

BIGTABLE FEATURES• Distributes data across many commodity servers

• Sorts data by key for fast lookup of values by key

• Scan across multiple key value pairs

• Highly consistent writes to single row

• Support for MapReduce jobs

DATA MODEL

Key

ValueRow ID

ColumnTimestamp

Family Qualifier

Row ID Col Fam Col Qual Timestamp Value

Bob Email id0023 20120301 Hey joe, can you send ...

Bob Email id0024 20120302 Re: next Thursday ...

Bob UserPrefs Background 20130101 Grey

Fred Email id0001 20080302 Welcome to gmail ...

Sarah Email id0004 20130201 Hi again ...

Sara Videos ytid009 20100303 nsu736:)jdudjdk$:)378;'$$)

Tablet servers HDFS DataNodesCommit Layer Replication Layer

SINCE 2006• Several BigTable implementations

• Apache Hbase

• Apache Cassandra

• Apache Accumulo

• others …

BIGTABLE IS BIGTABLE RIGHT?

HBASE

HBASE• Open source Apache project started by developers at

Powerset, bought by Microsoft

• Now used at Facebook, StumbleUpon, other big web sites

• Fast reads

• Row-oriented API

• Each column family has it's own set of files

CASSANDRA

CASSANDRA• Apache project started at Facebook

• Combines elements of BigTable and Amazon's Dynamo into one system

• Used at Netflix, other web sites

• Fast writes

• Tunable consistency

Tablet serversCommit and Replication Layer

CONSISTENCY

• Highly consistent means: writes in one place

• Eventually consistent: writes in > one place

• Writes in > one place: network partition tolerance

• Partition tolerance: geographically distributed servers

• *Google uses Spanner to synchronize multiple dbs

Tablet serversData Center A Data Center B

Data Center A Data Center BTablet servers

OVERVIEW

• Both highly scalable

• Used to build web applications that can serve millions of users at once

• Serves as a low-latency persistence layer for real time service of requests

• Available in single data center or cross data center options

USE CASE

• Most data comes from users

• Schema defined by the application

• Data builds up over time

Many UsersDbWeb

application

ACCUMULO

ACCUMULO

• Can support the web application use-case

• But what are those other extra features for?

ACCUMULO ‘EXTRAS’• Dynamic Column Families

• Column Visibility

• Key-value oriented API

• Iterators

• Batch Scanners

BIG ORGANIZATIONS

• Missions other than internet services

• Various disparate operational systems that generate data

• Desire to look across and analyze that data

• Desire to deliver results to their own population

USE CASE IS DISCOVERING AND ANALYZING ALL DATA

ISSUES

• Scale

• Unknown / multiple schema

• Support for analysis without data movement

• Varying levels of sensitivity in the same system

• Support a high number of low-latency user requests

Many Users

Analyze

Db

Data sets

SCALE?

CHECK (IT’S BIGTABLE)

NO CONTROL OVER OR MANY DIFFERENT SCHEMA?

MAP EXISTING FIELDS TO COLUMNS DYNAMICALLY

INCLUDING COLUMN FAMILIES

VARYING LEVELS OF DATA SENSITIVITY?

COLUMN VISIBILITY

DATA MODEL

Key

ValueRow ID

ColumnTime

stampFamily Qualifier Visibility

Row ID Col Fam Col Qual Col Vis Timestamp Value

Bob Email id0023 personal comms 20120301 Hey joe, can

you send ...

Bob Email id0024 personal comms 20120302 Re: next

Thursday ...

Bob UserPrefs Background prefs 20130101 Grey

Fred Email id0001 personal comms 20080302 Welcome to

gmail ...

Sarah Email id0004 personal comms 20130201 Hi again ...

Sara Videos ytid009 public post 20100303nsu736:)jdu

djdk$:)378;'$$)

DATA OF VARYING SENSITIVITY LEVELS CAN BE PHYSICALLY CO-LOCATED

FRAMEWORKS LIKE HADOOP MAP REDUCE LOVE IT WHEN

DATA IS ALL TOGETHER

LOOK ACROSS DATASETS?

SECONDARY INDICES

SECONDARY INDICES

• Application-created data: known

• Pre-existing data? unknown

DATA DISCOVERY!

SECONDARY INDICESRowID Col Qual Value

RID00001 age 54

RID00001 name bob

RID00002 name fred

RID00003 age 43

RID00003 height 5’9”

RID00003 name harry

RID00004 name carl

RID00005 name evan

RowID Col Fam Col Qual

43 age RID00003

54 age RID00001

5’9” height RID00003

bob name RID00001

carl name RID00004

evan name RID00005

fred name RID00002

harry name RID00003

PARTIAL ROW SCANS

BATCH SCANNERS

RowID Col Qual Value

RID00001 age 54

RID00001 name bob

RID00002 name fred

RID00003 age 43

RID00003 height 5’9”

RID00003 name harry

RID00004 name carl

RID00005 name evan

Batch Scanner

COLUMN VISIBILITY APPLIES TO INDEXES TOO

ANALYSIS?

MAPREDUCE: CHECK

SHUFFLE-SORTED?

• Between Map and Reduce phases is shuffle-sort

• Sorting by key is necessary so all the values for a given key end up next to each other …

• BigTable also sorts keys …

ITERATORS

Value combine(Iterator<Value> values)

PRE-COMPUTATION

Many Users

Analyze

Db

Data sets

ACCUMULO