Adam Fuchs' presentation slides on what's next in the evolution of BigTable implementations (transactions, indexing, etc.) and what these advances could mean for the massive database that gave rise to Google.

1. Securely explore your data
WHAT'S NEXT FOR
BIGTABLE?
Adam Fuchs, CTO
Sqrrl Data, Inc.
May 22, 2014

2. TODAY’S TALK
• History of the World: Part 3
• Bigtable/Accumulo Technology Overview
• Accumulo Demonstration
• Database Technology Survey
© 2014 Sqrrl Data, Inc. | All Rights Reserved 2

3. TIMELINE OF RELEVANT EVENTS
© 2014 Sqrrl Data, Inc. | All Rights Reserved
Google’s
BigTable Paper
2006
NSA Builds
Accumulo
2008
Sqrrl Founded
2012
1st Sqrrl Release
and Customers
2013
NSA Open
Sources
Accumulo
2011
3

4. Accumulo is a:
• Apache Software Foundation (ASF) Open-
Source Software Project
• Clone of Google’s Bigtable
• Secure, Sorted Key-Value Store
• Row-level ACID (locally) Distributed NoSQL
Database
© 2014 Sqrrl Data, Inc. | All Rights Reserved 4

5. Sqrrl is:
• A commercial software company located in
Cambridge, MA
• A search and Exploration Platform built with
Apache Accumulo
• An exciting startup with a long roadmap of
challenging problems to solve
• Hiring!
© 2014 Sqrrl Data, Inc. | All Rights Reserved 5

6. 6

7. BIGTABLE & ACCUMULO TECH
OVERVIEW
1. Data Model & API
2. Underlying Architecture
3. Distinguishing Features
© 2014 Sqrrl Data, Inc. | All Rights Reserved 7

8. 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
John Doe Notes PCP PCP_JD 20120912
Patient suffers
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
ACCUMULO DATA FORMAT
© 2014 Sqrrl Data, Inc. | All Rights Reserved 8

9. Instance
new ZooKeeperInstance(...)
new MockInstance()
Connector
getConnector(...)
TableOperations
InstanceOperations
SecurityOperations
Scanner BatchScanner
createScanner(...)createBatchScanner(...)
Range
IteratorOption
Map.Entry
Key Value
iterator()
BatchWriter
createBatchWriter(...)
Mutation
addMutation(...)
THE ACCUMULO CLIENT API
© 2014 Sqrrl Data, Inc. | All Rights Reserved 9

10. • Collections of KV pairs form Tables
• Tables are partitioned into Tablets
• Metadata tablets hold info about
other tablets, forming a 3-level
hierarchy
• A Tablet is a unit of work for a
Tablet Server
Data
Tablet
-­‐∞
:
thing
Data
Tablet
thing
:
∞
Data
Tablet
-­‐∞
:
Ocelot
Data
Tablet
Ocelot
:
Yak
Data
Tablet
Yak
:
∞
Data
Tablet
-­‐∞
to
∞
Table:
Adam’s
Table
Table:
Encyclopedia
Table:
Foo
ACCUMULO TABLETS
Well-­‐Known
Loca9on
(zookeeper)
Root
Tablet
-­‐∞
to
∞
Metadata
Tablet
2
“Encyclopedia:Ocelot”
to
∞
Metadata
Tablet
1
-­‐∞
to
“Encyclopedia:Ocelot”
© 2014 Sqrrl Data, Inc. | All Rights Reserved 10

11. Tablet
Server
Tablet
Tablet
Server
Tablet
Tablet
Server
Tablet
Applica9on
Zookeeper
Zookeeper
Zookeeper
Master
HDFS
Read/Write
Store/Replicate
Assign/Balance
Delegate
Authority
Delegate
Authority
Applica9on
Applica9on
ACCUMULO PROCESSES
© 2014 Sqrrl Data, Inc. | All Rights Reserved 11

12. In-­‐Memory
Map
Write
Ahead
Log
(For
Recovery)
Sorted,
Indexed
File
Sorted,
Indexed
File
Sorted,
Indexed
File
Tablet
Reads
Iterator
Tree
Minor
Compac<on
Merging
/
Major
Compac<on
Iterator
Tree
Writes
Iterator
Tree
Scan
TABLET DATA FLOW
© 2014 Sqrrl Data, Inc. | All Rights Reserved 12

13. Iterator Operations:
• File Reads
• Block Caching
• Merging
• Deletion
• Isolation
• Locality Groups
• Range Selection
• Column Selection
• Cell-level Security
• Versioning
• Filtering
• Aggregation
• Partitioned Joins
ITERATOR FRAMEWORK
© 2014 Sqrrl Data, Inc. | All Rights Reserved 13

14. WORD COUNT:
SUMMING AGGREGATING ITERATOR
Input Corpus
© 2014 Sqrrl Data, Inc. | All Rights Reserved 14

15. Ingesters QueriersTablet Servers
ACCUMULO LATENCIES
Input
Batch
Writer
In-
Memory
Map
Scan
Iterators
Scanner/
Batch
Scanner
In-
Memory
Map
RFile
Compactio
n
Iterators
Scan
Iterators
RFile
Compactio
n
Iterators
In-
Memory
Map
RFiles
Compactio
n
Iterators
Scan
Iterators
Output
~ms~ms ~ms
ms-min
© 2014 Sqrrl Data, Inc. | All Rights Reserved 15

16. ACCUMULO THROUGHPUT
Ingesters QueriersTablet Servers
Input
Batch
Writer
In-
Memory
Map
Scan
Iterators
Scanner
/Batch
Scanner
In-
Memory
Map
RFile
Compacti
on
Iterators
Scan
Iterators
RFile
Compacti
on
Iterators
In-
Memory
Map
RFiles
Compactio
n
Iterators
Scan
Iterators
Output
~ms~ms ~ms
ms-min
Scan:
~1M entries/s per
node
Ingest:
~200K entries/s
per node
Read-Modify-Write Latency: ~ms
ê
>1K entries/s challenging with R-M-W
© 2014 Sqrrl Data, Inc. | All Rights Reserved 16

17. Securely explore your data
DEMO

18. R-M-R VS. COMPACTION-TIME
AGGREGATION
Read/Modify/Write (HBase) vs. Iterators/Combiners (Accumulo)
© 2014 Sqrrl Data, Inc. | All Rights Reserved 18

19. SURVEY OF DATABASE
TECHNOLOGY
• Exercises in Center-Seeking
• SQL vs. NoSQL
• Ingest-time vs. Query-time Analytics
• ACID vs. BASE
• Normalized vs. Denormalized Data Models
• Primary Use Cases for Sqrrl+Accumulo
© 2014 Sqrrl Data, Inc. | All Rights Reserved 19

20. SQL VS. NOSQL
NoSQL
• Optimized for get/put
operations
• Specialized for client
languages
• High concurrency
• More client-side
control
Hybrid
• Extend and evolve
SQL
• Standardize and
incorporate NoSQL
paradigms
SQL
• Optimized for joins
• Strong mathematical
roots in set theory
• Automatic query
optimization
© 2014 Sqrrl Data, Inc. | All Rights Reserved 20

21. INGEST-TIME VS. QUERY-TIME
ANALYTICS
Ingest-Time
• Optimized for online
statistics
• Can reduce storage
footprint
• Can be indexed for
low latency
• Leverages a variety
of indexes
• Requires extensive
data organization at
ingest
Hybrid
• Create partial
summary at ingest
(Question-focused
datasets, knowledge
bases, etc.)
• Support ad-hoc
queries over
summaries
• Leverage all known
indexing strategies **
Query-Time
• Can compute holistic
statistics, like ranking,
topN, etc.
• Ad-hoc analytics:
don’t know the query
ahead of time
• High latency and low
concurrency at scale
• Leverages block
indexes, columnar
layout
• Ingest can be “stream
to disk”
© 2014 Sqrrl Data, Inc. | All Rights Reserved 21

22. ACID VS. BASE
ACID
• Atomicity: all or
nothing for a group of
operations
• Consistency and
Isolation: support
simple reasoning for
distributed,
multithreaded clients
• Durability: simple
reasoning for whether
data might be lost
Hybrid
• Must make some
relaxations for
performance at scale
(under failure modes)
• Many options for
“Lightweight”
transaction support
• Accumulo limits
atomicity,
consistency, and
isolation to row-level
operations
BASE
• Basically Available:
ensure that core
operations always
complete in an
advertised time
• Soft-State: relaxation
of referential integrity,
etc.
• Eventual
Consistency:
relaxation of
© 2014 Sqrrl Data, Inc. | All Rights Reserved 22

23. NORMALIZED VS. DENORMALIZED
DATA MODELS
Normalized
• “Normal Form
Relational Database”
• Minimizes data
footprint
• Minimizes cost of
data maintenance
• Can lead to
expensive joins at
query time
Hybrid
• Start with document
store
• Introduce links/edges
for quick joins
• Dynamically adapt to
flexible or sparse
schemas
• Similar to property
graphs
Denormalized
• “Document Store”
• Flexible schema lets
applications adapt
quickly to changing
environments
• Pre-joined to
eliminate joins at
query-time
• Optimized for
“append-only” data
• Can inflate data sizes
and slow data ingest
© 2014 Sqrrl Data, Inc. | All Rights Reserved 23

24. KNOWLEDGE-BASE USE CASE
2014-04-14
06:36:09 429
73.105.179.202
username@msn.c
om 500 POST
application/json
2014-04-14 06:36:09 429 73.105.179.202 username@msn.com 500 POST application/json
HTTPS “wikipedia.org:443/grouchinesses/?215=felled&297=wading&768=shimmies...” "Mozilla/
5.0 (Macintosh; Intel Mac OS X 10_8_3) AppleWebKit/537.31 (KHTML, like Gecko) Chrome/
26.0.1410.43 Safari/537.31” 208.80.152.201
HR
Netflow
Proxy Logs
HTTPS “wikipedia.org:
443/grouchinesses/?
215=felled&297=wadin
g&768=shimmies...”
"Mozilla/5.0 (Macintosh;
Intel Mac OS X 10_8_3)
AppleWebKit/537.31
(KHTML, like Gecko)
Chrome/26.0.1410.43
Safari/537.31”
208.80.152.201
Email
Social Media
© 2014 Sqrrl Data, Inc. | All Rights Reserved 24

25. STREAM PROCESSING USE CASE
© 2014 Sqrrl Data, Inc. | All Rights Reserved
Dashboards
Actions
Interactive
Analysis Tools
(Discovery + Forensics)
1. SPE queries Sqrrl to enrich streaming data
2. SPE persists results in Sqrrl for future query
3. SPE takes action automatically
4. SPE issues data-driven alerts
5. Sqrrl provides context for dashboards
6. Analysis tools query use Sqrrl to search and
manipulate historical data
DATA
SPE
25

26. SQRRL OPERATIONALIZES
ACCUMULO WITH...
© 2014 Sqrrl Data, Inc. | All Rights Reserved 26
Data-Centric Security
Petabyte Scale and Operational Speeds
Document and Graph Data Models
SqrrlQL, including Aggregates, Secure Full-
Text Search, and Secure Graph Search
Analytics, including Real-Time Statistics and
Hadoop Integrations

27. MODERNIZING VISUALIZATION
© 2014 Sqrrl Data, Inc. | All Rights Reserved 27
Sqrrl is building the next generation of
operational analytics visualizations

28. UPCOMING EVENTS
Accumulo Summit 2014
• June 12 in College Park, MD
• http://accumulosummit.com
• Multiple tracks of talks from the leaders of the Accumulo community
IEEE HPEC Conference 2014
• September 9-11 in Waltham, MA
• http://www.ieee-hpec.org/
• Accumulo Users Group Meeting as a Special Event
• Accumulo tutorial
Watch for more meetup opportunities coming soon!
© 2014 Sqrrl Data, Inc. | All Rights Reserved 28