IMCSummit 2015 - 1 IT Business - The Evolution of Pivotal Gemfire

2© Copyright 2015 Pivotal. All rights reserved. 2© Copyright 2015 Pivotal. All rights reserved.
Evolution of Pivotal Gemfire
Which way might the "Apache Way” take It?
Milind Bhandarkar
milind@ampool.io
CEO, Ampool Inc.
@techmilind
Roman Shaposhnik
rvs@apache.org
Director of Open Source, Pivotal Inc.
@rhatr

3© Copyright 2015 Pivotal. All rights reserved.
Topics
Ÿ  GemFire history, architecture, and use cases
Ÿ  Geode as the open source core of GemFire
Ÿ  Requirements of the modern data infrastructure
Ÿ  Butterfly architecture
Ÿ  Geode as an engine for in-memory data exchange layer

It’s not the size of
DATAit’s how you use it!

2004 2008 2014
•  Massive increase in data
volumes
•  Falling margins per
transaction
•  Increasing cost of IT
maintenance
•  Need for elasticity in
systems
•  Financial Services
Providers (every major
Wall Street bank)
•  Department of Defense
•  Real Time response needs
•  Time to market constraints
•  Need for flexible data
models across enterprise
•  Distributed development
•  Persistence + In-memory
•  Global data visibility needs
•  Fast Ingest needs for data
•  Need to allow devices to
hook into enterprise data
•  Always on
•  Largest travel Portal
•  Airlines
•  Trade clearing
•  Online gambling
•  Largest Telcos
•  Large mfrers
•  Largest Payroll processor
•  Auto insurance giants
•  Largest rail systems on
earth
Our GemFire Journey Over The Years

Apps at Scale Have Unique Needs
Pivotal GemFire is the distributed, in-memory database for
apps that need:
1.  Elastic scale-out performance
2.  High performance database capabilities in distributed systems
3.  Mission critical availability and resiliency
4.  Flexibility for developers to create unique applications
5.  Easy administration of distributed data grids

1. Elastic Scale-Out Performance
Nodes
Ops /
SecLinear scalability
Elastic capacity +/-
Latency-minimizing
data distribution
China Railway
Corporation
“The system is operating with solid
performance and uptime. Now, we have a
reliable, economically sound production
system that supports record volumes and
has room to grow”
Dr. Jiansheng Zhu, Vice Director of China Academy
of Railway Sciences
•  4.5 million ticket purchases & 20
million users per day.
•  Spikes of 15,000 tickets sold per
minute, 40,000 visits per second.

2. High Performance Database Capabilities
Performance-
optimized
persistence
Configurable
consistency Partitioned Replicated Disabled
Distributed &
continuous queries
Distributed transactions, indexing, archival
Newedge
“Our global deployment of Pivotal
GemFire’s distributed cache gives me a
single version of the trade – resolving
hard-to-test-for synchronization issues
that exist within any globally distributed
business application architecture”
Michael Benillouche, Global Head of Data
Management

3. Mission Critical Availability and Resiliency
Cluster to cluster
WAN connectivity
Cluster resilience
& failover
XX Gire
“We can track and collect money at our
4,000+ kiosks and branches – even without
a reliable Internet connection. GemFire
provides the core data grid and a significant
amount of related functionality to help us
handle this unreliable network problem”
Gustavo Valdez, Chief of Architecture and
Development
•  19 million payment transactions per
month
•  4000+ points of sale with intermittent
Internet connectivity

4. Flexibility for Developers to Create Unique
Apps
•  Data Structures:
–  User-defined classes
–  Documents (JSON)
•  Native language support:
–  Java, C++, C#
•  API’s
–  Java: Hashmap
–  Memcache
–  Spring Data GemFire
–  C++ Serialization API’s
•  Embedded query authoring
–  Object Query Language (OQL)
•  Publish & subscribe framework for
continous query & reliable
asynchronous event queues
•  App-server embedded functionality:
–  Web app session state caching
–  L2 Hibernate

What makes it fast?
Ÿ  Design center is RAM not HDD
Ÿ  Really demanding customers
Ÿ  Avoid and minimize, particularly on the critical read/write paths:
–  Network hops, copying data, contention, distributed locking, disk seeks, garbage
Ÿ  Lots and lots of testing
–  Establish and monitor performance baselines
–  Distributed systems testing is difficult!

Horizontal Scaling for GemFire (Geode) Reads
With Consistent Latency and CPU
•  Scaled from 256 clients and 2 servers to 1280 clients and 10 servers
•  Partitioned region with redundancy and 1K data size
0
2
4
6
8
10
12
14
16
18
0
1
2
3
4
5
6
2 4 6 8 10
Speedup
Server
Hosts
speedup
latency
(ms)
CPU
%

GemFire (Geode) 3.5-4.5X Faster Than Cassandra
for YCSB

Apache Geode
(incubating)
The open source core of GemFire

Geode Will Be A Significant Apache Project
Ÿ  Over a 1000 person years invested into cutting edge R&D
Ÿ  Thousands of production customers in very demanding verticals
Ÿ  Cutting edge use cases that have shaped product thinking
Ÿ  Tens of thousands of distributed , scaled up tests that can randomize
every aspect of the product
Ÿ  A core technology team that has stayed together since founding
Ÿ  Performance differentiators that are baked into every aspect of the
product

Geode or GemFire?
Ÿ  Geode is a project, GemFire is a product
Ÿ  We donated everything but the kitchen sink*
Ÿ  More code drops imminent; going forward all development
happens OSS-style (“The Apache Way”)
* Multi-site WAN replication, continuous queries, and native (C/C++) client driver

Why OSS? Why Now? Why Apache?
Ÿ  Open Source Software is fundamentally changing buying patterns
–  Developers have to endorse product selection (No longer CIO handshake)
–  Open source credentials attract the best developers
–  Open Source has replaced standards
Ÿ  Align with the tides of history
–  Customers increasingly asking to participate in product development
–  Allow product development to happen with full transparency
Ÿ  Apache Way
–  “Community over code”
–  Use cases far beyond Pivotal Gemfire’s

Beyond Gemfire’s core

Roadmap
Ÿ  HDFS persistence
Ÿ  Off-heap storage
Ÿ  Lucene indexes
Ÿ  Spark integration
Ÿ  Cloud Foundry service
…and other ideas from the Geode community!

Geode in modern data
infrastructure

Infrastructure is increasingly Scale-Out

Memory throughput growth

Memory hierarchy getting deeper

One-size-fits-all Data Platform Era is Over

Analytics moving from Batch to Real-Time

What happened?

And, then…
HDFS
ASF Projects FLOSS Projects Pivotal Products
MapReduce

In a blink of an eye…
HDFS
Pig
Sqoop Flume
Coordination and
workflow
management
Zookeeper
Ambari &
Command Center
GemFire XD
Oozie
MapReduce
Hive
Tez
Giraph
Hadoop UI
Hue
SolrCloud
Phoenix
HBase
Crunch Mahout
Spark
Shark
Streaming
MLib
GraphX
HAWQ
SpringXD
MADlib
Hamster
PivotalR
YARN
ASF Projects FLOSS Projects Pivotal Products

Data Lake 1.0

… and Now

The missing building block

Sharing operational data at the speed of RAM
In-Memory Data Exchange Layer
HDFS Isilon…backend archival stores…
Spark HAWQ…frontend processing frameworks…

Spark’s view on how to lock you in
HAWQ…frontend processing frameworks…Spark
Spark

HDFS’s view on how to lock you in
HAWQ…frontend processing frameworks…Spark
HDFS

How is open source solving this?

Short list of open source contenders
Ÿ  Tachyon
Ÿ  Infinispan
Ÿ  Apache Ignite (incubating)
Ÿ  Apache Geode (incubating)

Geode’s secret sauce
Ÿ  Community!
Ÿ  Maturity
Ÿ  Scalability
Ÿ  Building blocks

A few key building blocks
•  PDX Serialization
•  Asynchronous Events

Fixed or flexible schema?
id name age pet_id
{
id : 1,
name : “Fred”,
age : 42,
pet : {
name : “Barney”,
type : “dino”
}
}
OR

But how to serialize data?
http://blog.pivotal.io/pivotal/products/data-serialization-how-to-run-multiple-big-data-apps-at-once-with-gemfire

Efficient for queries
SELECT p.name from /Person p WHERE p.pet.type = “dino”
{
id : 1,
name : “Fred”,
age : 42,
pet : {
name : “Barney”,
type : “dino”
}
}
single field
deserialization

Easy to use
Ÿ  Access from Java, C#, C++, JSON
Ÿ  Domain objects not required
Ÿ  Automatic type definition
–  No IDL compiler or schema required
–  No hand-coded read/write methods

Asynchronous Events – Design Goals
Ÿ  High availability
Ÿ  Low latency, high throughput
Ÿ  Deliver events to a receiver without impacting the write path

Questions?
Ÿ  http://geode.incubator.apache.org
Ÿ  dev@geode.incubator.apache.org
Ÿ  user@geode.incubator.apache.org
Ÿ  http://github.com/apache/incubator-geode

Bonus Content

PDX

Distributed type registry
Member A Member B
Distributed Type Definitions
Person p1 = …
region.put(“Fred”, p1);

Member A Member B
Person p1 = …
automatic
definition

Member A Member B
Person p1 = …
automatic
definition
replicate serialized
data containing typeId

Schema evolution
PDX provides forwards and backwards
compatibility, no code required
Member A Member B
v1 v2
Application
#2
Application
#1
v2 objects preserve data
from missing fields
v1 objects use default values
to fill in new fields

Asynchronous Events

Member 3
Member 1
Serial Queues
Member 2
LOL!!put
Primary Queue
Secondary Queue
Enqueue
sup
LOL!!
sup
sup
LOL!!
Replicate

Member 3
Member 1
Member 2
Serial Queues
sup
LOL!!
sup
LOL!!
AsyncEventListener

{

processBatch()

}

LOL!!
sup
Dispatch Events
from Primary

put
Secondary Queue
(Partition 1)
LOL!!
sup
sup
LOL!!
Primary Queue
(Partition 2)
Parallel Queues
Primary Queue
(Partition 1)
LOL!!
sup
sup
LOL!!
Word
Word

LOL!!
sup
sup
LOL!!
Parallel Queues
LOL!!
sup
sup
LOL!!
Word
Word
AsyncEventListener

{

processBatch()

}

AsyncEventListener

{

processBatch()

}

Parallel
Dispatch

“There are only two hard things in Computer Science: cache
invalidation, naming things, and off-by-one errors.”
– the internet

Client driver
Ÿ  Intelligent – understands data distribution for single hop
network access
Ÿ  Caching – can be configured to locally cache data for even
faster access
Ÿ  Events – registerInterest() in keys to receive push
notifications

Client subscriptions
Ÿ  Useful for refreshing client cache, pushing events (“topics”)
to multiple clients
Ÿ  Highly available & scalable via in-memory replicated queues
Ÿ  Events are ordered, at-least-once delivery
Ÿ  Durable subscriptions, conflation optional

Member A Member B
region.registerInterest(“Fred”);
Client
Client

Member A Member B
Person p1 = …
Client
Client

Use cases and patterns

“Low touch” Usage Patterns
Simple template for TCServer, TC, App servers
Shared nothing persistence, Global session state
HTTP Session management
Set Cache in hibernate.cfg.xml
Support for query and entity caching
Hibernate L2 Cache plugin
Servers understand the memcached wire protocol
Use any memcached client
Memcached protocol
<bean id="cacheManager"
class="org.springframework.data.gemfire.support.GemfireCacheManager"Spring Cache Abstraction

Application Patterns
Ÿ  Caching for speed and scale
–  Read-through, Write-through, Write-behind
Ÿ  Geode as the OLTP system of record
–  Data in-memory for low latency, on disk for durability
Ÿ  Parallel compute engine
Ÿ  Real-time analytics

Development Patterns
Ÿ  Configure the cluster programmatically or declaratively
using cache.xml, gfsh, or SpringDataGemFire

<cache>

<region
name="turbineSensorData"
refid="PARTITION_PERSISTENT">

<partition-‐attributes
redundant-‐copies="1"
total-‐num-‐buckets="43"/>

</region>

</cache>

Ÿ  Write key-value data into a Region using { create | put |
putAll | remove }
–  The value can be flat data, nested objects, JSON, …

Region
sensorData
=
cache.getRegion("TurbineSensorData");

SensorKey
key
=
new
SensorKey(31415926,
"2013-‐05-‐19T19:22Z");
//
turbineId,
timestamp

sensorData.put(key,
new
TurbineReading()

.setAmbientTemp(75)

.setOperatingTemp(80)

.setWindDirection(0)

.setWindSpeed(30)

.setPowerOutput(5000)

.setRPM(5));

Ÿ  Read values from a Region by key using { get | getAll }

TurbineReading
data
=
sensorData.get(new
SensorKey(31415926,"2013-‐05-‐19T19:22Z"));
Ÿ  Query values using OQL

//
finds
all
sensor
readings
for
the
given
turbine

SELECT
*
from
/TurbineSensorData.entrySet
WHERE
key.turbineId
=
31415926

//
finds
all
sensor
readings
where
the
operating
temp
exceeds
a
threshold

SELECT
*
from
/TurbineSensorData.entrySet
WHERE
value.operatingTemp
>
120

Ÿ  Apply indexes to optimize queries

Ÿ  Execute functions to operate on local data in parallel
Ÿ  Respond to updates using CacheListeners and Events
Ÿ  Automatic redundancy, partitioning, distribution, consistency,
network partition detection & recovery, load balancing, …

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