Getting Spark ready for real-time, operational analytics

SnappyData Confidential – Do Not Distribute
SnappyData
Getting Spark ready for real-time,
operational analytics
www.snappydata.io
Suds Menon
Co-Founder SnappyData
March 2016

Because Insights are perishable and degrade over time
The New Arms Race
www.snappydata.io
●  Sift through data to get insights
to improve your business
●  What is your time to insights?
●  What is your time to
operationalizing insights?
DATA, THE NEW OIL

Every enterprise today deals with these 4 kinds of data interactions
The Four Horsemen Of Data
www.snappydata.io
OLTP OLAP Streaming Machine
Learning

Who Are We?
●  An EMC-Pivotal spinout focused on real time operational
analytics
●  New Spark-based open source project started by Pivotal
GemFire founders+engineers
●  Decades of in-memory data management experience
●  Focus on real-time, operational analytics: Spark inside an
OLTP+OLAP database
www.snappydata.io

SnappyData At Cruising Altitude
Real time operational Analytics – TBs in memory
Single unified HA cluster: OLTP + OLAP +
Stream for real-time analytics
Batch design, high throughput
RDB
Rows
Txn
Columnar
API
Stream processing
ODBC,
JDBC, REST
Spark -
Scala, Java,
Python, R
HDFS
AQP
First commercial project on Approximate
Query Processing(AQP)
MPP DB
Index

SnappyData: A new approach
Single unified HA cluster: OLTP + OLAP + Stream
for real-time analytics
Batch design, high throughput
Real-‐time

design
center

-‐
Low
latency,
HA,

concurrent

Vision: Drastically reduce the cost and
complexity in modern big data

Huge community adoption, slip streaming into Hadoop momentum, great data integration platform
Why Spark?
•  Most events in life can be analyzed as micro batches
•  Blends streaming, interactive, and batch analytics
•  Appeals to Java, R, Python, Scala programmers
•  Rich set of transformations and libraries
•  RDD and fault tolerance without replication
•  Offers Spark SQL as a key capability
www.snappydata.io

Spark is a compute framework that processes data, not an analytics database
Clearing Up Some Spark Myths
www.snappydata.io
●  It is NOT a distributed in-memory database
○  It’s a computational framework with immutable caching
●  It is NOT Highly Available
○  Fault tolerance is not the same as HA
●  NOT well suited for real time, operational environments
○  Does not handle concurrency well
○  Does not share data very well either

SnappyData & Lambda
SnappyData Focus

Perspective on Lambda for real time
In-Memory DB
Interactive queries,
updates
Deep Scale, High
volume
MPP DB
Transform
Data-in-motion
Analytics
Application
Streams
Alerts

RELEVANT USECASES
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Use Case Patterns
•  Stream ingestion database for spark
Process streams, transform, real-time scoring, store, query
•  In-memory database for apps
Highly concurrent apps, SQL cache, OLTP + OLAP
•  Analytic caching pattern
Caching for Analytics over any “Big data” store (esp MPP)
Federate query between samples and backend

Typical Use Case Patterns
www.snappydata.io
•  Market Surveillance Systems (Trading exchanges, Market makers)
•  Real Time Scoring Systems (Product recommendations, real time offers)
•  Telco Analytics (Location based services, Predictive analytics)
•  Sensor Analytics (Real time alerting for parking management, lighting etc.)
•  Ad analytics + Ad placement systems
•  Combining structured and unstructured analytics (SQL + ML)

Market Surveillance
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Identify patterns
based on query
results
Partitioned, HA
stream ingestion
Prevent
settlement,
investigate further
SQL queries &
Stream Analytics
on microbatches

Contextual Marketing
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Pick Ad based on
variety of reference
data parameters
Transactional
request for Ad
placement
Deliver in real
time
Join with history, join
with user profile, join
with location

Location Based Telco Services
www.snappydata.io
Geo Fencing Mobile Marketing Network Analytics
●  INGEST, CORRELATE, JOIN WITH HISTORICAL DATA,
RESPOND

Spark Architecture
Driver
Cluster
Manager
(YARN,
Mesos,
Standalone)
Worker
Worker
Worker
Executor

REST API for
Job
Submission
Worker
Worker
Worker
Data Server
Executor
Cluster
Manager
(YARN,
Mesos,
Standalone)
Data Server
Executor
Snappy Infused Spark Architecture
JDBC Clients
ODBC Clients
Job ServerLead Node
Lead Node

Core Components Of SnappyData

Synergistic with BDS & CF
Spark Based Snappy Core HAWQ/GreenPlum

Colocated row/column Tables in Spark
Row
Table
Column
Table
Spark
Executor
TASK
Spark Block Manager
Stream
processing
Row
Table
Column
Table
Spark
Executor
TASK
Spark Block Manager
Stream
processing
Row
Table
Column
Table
Spark
Executor
TASK
Spark Block Manager
Stream
processing
●  Spark Executors are long lived and shared across multiple apps
●  Gem Memory Mgr and Spark Block Mgr integrated

Table can be partitioned or replicated
Replicated
Table
Partitioned
Table
(Buckets A-H) Replicated
Table
Partitioned
Table
(Buckets I-P)
consistent replica on each node
Partition
Replica
(Buckets A-H)
Replicated
Table
Partitioned
Table
(Buckets Q-W)Partition
Replica
(Buckets I-P)
Data partitioned with one or more replicas

Linearly scale with shared partitions
Spark Executor
Spark Executor
Kafka
queue
Subscriber N-Z
Subscriber A-M
Subscriber A-M
Ref data
Linearly scale with partition pruning
Input queue,
Stream, IMDB,
Output queue
all share the
same
partitioning
strategy

Point access, updates, fast writes
●  Row tables with PKs are distributed HashMaps
○  with secondary indexes
●  Support for transactional semantics
○  read_committed, repeatable_read
●  Support for scalable high write rates
○  streaming data goes through stages
○  queue streams, intermediate storage (Delta row buffer),
immutable compressed columns

SQL And Spark API Support

Full Spark Compatibility
●  Any table is also visible as a DataFrame
●  Any RDD[T]/DataFrame can be stored in SnappyData
tables
●  Tables appear like any JDBC sourced table
○  But, in executor memory by default
●  Addtional API for updates, inserts, deletes
//Save a dataFrame using the spark context …
context.createExternalTable(”T1", "ROW", myDataFrame.schema, props );
//save using DataFrame API
dataDF.write.format("ROW").mode(SaveMode.Append).options(props).saveAsTable(”T1");

Can we use Statistical methods to shrink data?
•  It is not always possible to store all the data
Many applications (telecoms, ISPs, search engines) can’t keep
everything
•  It is inconvenient to work with data in full
•  It is faster to work with a compact summary
Better to explore data on a laptop than a cluster
Ref: Graham Cormode - Sampling for Big Data
Can
we
use
statistical
techniques
to
understand
data,
synthesize

something
relatively
small
but
still
answer
Analytical
queries?

Key feature: Synopses Data
●  Maintain stratified samples
○  Intelligent sampling to keep error bounds low
●  Probabilistic data
○  TopK for time series (using time aggregation CMS, item
aggregation)
○  Histograms, HyperLogLog, Bloom Filters, Wavelets
CREATE SAMPLE TABLE sample-table-name USING columnar
OPTIONS (
BASETABLE ‘table_name’ // source column table or stream table
[ SAMPLINGMETHOD "stratified | uniform" ]
STRATA name (
QCS (“comma-separated-column-names”)
[ FRACTION “frac” ]
),+ // one or more QCS

www.snappydata.io
Spot The Differences

Performance – Spark vs Snappy (TPC-H)
See ACM Sigmod 2016 paper for details
Available on snappydata.io blogs

Performance – Snappy vs in-memoryDB (YCSB)

Unified OLAP/OLTP streaming w/ Spark
●  Far fewer resources: TB problem becomes GB.
○  CPU contention drops
●  Far less complex
○  single cluster for stream ingestion, continuous queries, interactive
queries and machine learning
●  Much faster
○  compressed data managed in distributed memory in columnar
form reduces volume and is much more responsive

www.snappydata.io
SnappyData is Open Source
●  Beta will be on github before January. We are looking for
contributors!
●  Learn more & register for beta: www.snappydata.io
●  Connect:
○  twitter: www.twitter.com/snappydata
○  facebook: www.facebook.com/snappydata
○  linkedin: www.linkedin.com/snappydata
○  slack: http://snappydata-slackin.herokuapp.com
○  IRC: irc.freenode.net #snappydata

Q&A
www.snappydata.io

Getting Spark ready for real-time, operational analytics

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