Oct 2012 HUG: Apache Accumulo: Unlocking the Power of Big Data

sqrrl data, INC.
Secure. Scale. Adapt.

Adam Fuchs, Chief Technology Officer

info@sqrrl.com | @sqrrl_inc | 617.520.4375 sqrrl data, INC., All Rights Reserved

Who We are

is the commercial
provider of

Mature Database Technology - Apache Accumulo
Fine-Grained Access Controls - Data Integration and Sharing
Proven Performance - Petabytes and Beyond
Advanced Analytics - Search, Statistics, and Graphs

2

Contents

Core Philosophy
Technology
Techniques
Application APIs

3

Apache Accumulo Perspective

Data Data Data
Integration across:

Multiple business lines
Multiple data sets
Multiple applications
Multiple security, privacy, legal,
Application Application Application
policy, regulatory, and
compliance constraints
New demands

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Accumulo Design Drivers

Cell-Level Security
1  Express common security requirements in the infrastructure, not just in the application
 Data-centric approach encourages secure sharing

Scalability
2  Near linear performance improvements at thousands of nodes
 Durable and reliable under increased failures that come with scale

Diverse, Interactive Analytics
3  Sorted key/value core performs well in a diverse set of domains
 Information retrieval, statistics, graph analysis, geo indexing, and more

Flexible, Adaptive Schema
4  Start with universal structures and indexing
 Refine the schema over time

5

Contents

Core Philosophy
Technology
Techniques
Application APIs

6

Accumulo Key Structure

An Accumulo key is a 5-tuple, consisting of:
Row: Controls Atomicity
Column Family: Controls Locality
Column Qualifier: Controls Uniqueness
Visibility Label: Controls Access
Timestamp: Controls Versioning

Row Col. Fam. Col. Qual. Visibility Timestamp Value
Patient suffers
John Doe Notes PCP PCP_JD 20120912
from an acute …
John Doe Test Results Cholesterol JD|PCP_JD 20120912 183
John Doe Test Results Mental Health JD|PSYCH_JD 20120801 Pass
John Doe Test Results X-Ray JD|PHYS_JD 20120513 1010110110100…

Accumulo Key/Value Example

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Visibility Syntax & Semantics

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Tablets
Well-Known
Location
(zookeeper)
Collections of KV pairs form Tables
Tables are partitioned into Tablets
Root Tablet
-∞ to ∞ Metadata tablets hold info about
other tablets, forming a 3-level
hierarchy
Metadata Tablet 1 Metadata Tablet 2 A Tablet is a unit of work for a Tablet
-∞ to “Encyclopedia:Ocelot” “Encyclopedia:Ocelot” to ∞ Server

Table: Adam’s Table Table: Encyclopedia Table: Foo

Data Tablet Data Tablet Data Tablet Data Tablet Data Tablet Data Tablet
-∞ : thing thing : ∞ -∞ : Ocelot Ocelot : Yak Yak : ∞ -∞ to ∞

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Accumulo Architecture
Delegate
Zookeeper Authority Tablet Server
Zookeeper
Zookeeper
Tablet
Delegate Read/Write
Application
Authority Tablet Server
Assign/Balance

Master Application
Tablet

Store/Replicate Application
Tablet Server

Hadoop
Tablet

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Tablet Data Flow

Tablet
Scan
In-Memory Iterator
Reads
Writes Iterator Tree
Map Minor Tree

Compaction

Sorted, Ind Sorted, Ind
exed File exed File

Write Ahead Sorted, Ind
Log Iterator exed File
(For Recovery) Merging / Major Tree
Compaction

11

Contents

Core Philosophy
Technology
Techniques
Application APIs

16

Hierarchical Decomposition

Row: <person>

Column Family: attribute purchases returns

Column Qualifier: age discount sneakers hat

Value: <age> <40%> <cost> <cost>

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Materialized Table
Key/Value Pair
Row: bill george

Column attribute purchases attribute purchases returns
Family:

Column age discount sneakers age sneakers hat
Qualifier:

Value: 49 40% $100 27 $83 $42

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Forward and Inverted Index

Table: Forward Index Inverted Index

Row: <UUID> <Term>

Column Family: <Type> <Type> + <Field>

Column Qualifier: <Field> <UUID>

Value: <Term> <Digest of Event>

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Forward and Inverted Index

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Graph Analysis

Table: Graph Table

Row: <Node ID>

Column Family: “Node Info” “Out Edges” “In Edges”

Column Qualifier: <Field> <Node ID> <Node ID>
(Tuples):
<Edge ID> <Edge ID>

Value: <Value> <Edge Info> <Edge Info>

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Geospatial Queries
Table: Geo Index Latitude Longitude Depth
10110101001 00111010010 11010110110

Row: <GeoHash>
101001110111010101011100001011100

Column Family: <Event Type>

Column Qualifier: <UUID>

Value: <Digest of Event>

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Document Partitioning

Table: Shard Table

Row: <Partition ID>

Column Family: “Docs” “Inv. Index” “Field Index” “Geo”

Column Qualifier <UUID> <Term> <Field:Term> <Hash>
(Tuples):
<Field> <UUID> <UUID> <UUID>

Value: <Value>

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Document Partitioning

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Intersecting Iterator
‘foo’ and (‘bar’ or ‘baz’)

<Partition ID>

“Docs” “Inv. Index”

<UUID> <Term>

<Field> <UUID>

<Value>

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Contents

Core Philosophy
Technology
Techniques
Application APIs

27

acorn

Key/Value pairs are great! =
How do I construct a document
partitioning key again?
Techniques should be built into an API
Let the people have polyglot
Lucene, SQL, SPARQL, JAQL, Matlab
(not just Key, Value, Range)
+
+
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Combined IR + Graph Search

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Schema-less Stats

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Get Involved

http://accumulo.apache.org
Help us make Accumulo even better!

31

Contact

Adam Fuchs, CTO

sqrrl data, Inc.
617-520-4375
www.sqrrl.com
@sqrrl_inc
info@sqrrl.com

32

Oct 2012 HUG: Apache Accumulo: Unlocking the Power of Big Data

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