This presentation discusses architectural differences between Apache Apex features with Spark Streaming. It discusses how these differences effect use cases like ingestion, fast real-time analytics, data movement, ETL, fast batch, very low latency SLA, high throughput and large scale ingestion. Also, it will cover fault tolerance, low latency, connectors to sources/destinations, smart partitioning, processing guarantees, computation and scheduling model, state management and dynamic changes. Further, it will discuss how these features affect time to market and total cost of ownership.

1. Architectural Comparison of
Apache Apex and Spark Streaming
Webinar
June 8th 2016
Thomas Weise, Apache Apex PMC
@thweise thw@apache.org

2. Next Gen Stream Data Processing
• Data from variety of sources (IoT, Kafka, files, social media etc.)
• Unbounded, continuous data streams
ᵒ Batch can be processed as stream (but a stream is not a batch)
• (In-memory) Processing with temporal boundaries (windows)
• Stateful operations: Aggregation, Rules, … -> Analytics
• Results stored to variety of sinks or destinations
ᵒ Streaming application can also serve data with very low latency
2
Browser
Web Server
Kafka Input
(logs)
Decompress,
Parse, Filter
Dimensions
Aggregate Kafka
Logs
Kafka

3. Batch vs. Streaming
Credit: Gyula Fóra & Márton Balassi: Large-Scale Stream Processing in the Hadoop Ecosystem
3

4. Architecture and Features
Spark Streaming Apex
Model micro-batch native streaming/data-in-motion
Language Java, Scala, client bindings Java (Scala)
API declarative compositional (DAG), declarative*
Locality data locality advanced processing locality
Latency high very low (millis)
Throughput very high very high
Scalability scheduler limit horizontal
Partitioning standard advanced (parallel pipes, unifiers)
Connector Library Limited (certification),
externally maintained
Rich library of connectors and
processors, part of Apex (Malhar)
4

5. Operability
Spark Streaming Apex
State Management RDD, user code checkpointing
Recovery RDD lineage incremental (buffer server)
Processing Sem. exactly-once* end-to-end exactly-once
Backpressure user configuration Automatic (buffer server memory
+ disk)
Elasticity yes w/ limited control yes w/ full user control
Dynamic topology no yes
Security Kerberos Kerberos, RBAC*, LDAP*
Multi-tennancy depends on cluster manager YARN, full isolation
DevOps tools basic REST API, DataTorrent RTS
5

6. Apache Apex Features
• In-memory stream processing platform
ᵒ Developed since 2012, ASF TLP since 04/2016
• Unobtrusive Java API to express (custom) logic
• Scale out, distributed, parallel
• High throughput & low latency processing
• Windowing (temporal boundary)
• Reliability, fault tolerance, operability
• Hadoop native
• Compute locality, affinity
• Dynamic updates, elasticity
6

7. Apex Platform Overview
7

8. Apache Apex Malhar Library
8

9. Application Development Model
9
 A Stream is a sequence of data tuples
 A typical Operator takes one or more input streams, performs computations & emits one or more output streams
• Each Operator is YOUR custom business logic in java, or built-in operator from our open source library
• Operator has many instances that run in parallel and each instance is single-threaded
 Directed Acyclic Graph (DAG) is made up of operators and streams
Directed Acyclic Graph (DAG)
Output
Stream
Tupl
e
Tupl
e
er
Operator
er
Operator
er
Operator
er
Operator
er
Operator
er
Operator

10. Streaming Windows
10
 Application window
 Sliding window and tumbling window
 Checkpoint window
 No artificial latency

11. Event time & Dimensions Computation
11
(All) : 5
t=4:00 : 2
t=5:00 : 3
k=A, t=4:00 : 2
k=A, t=5:00 : 1
k=B, t=5:00 : 2
(All) : 4
t=4:00 : 2
t=5:00 : 2
k=A, t=4:00 : 2
K=B, t=5:00 : 2
k=A
t=5:00
(All) : 1
t=4:00 : 1
k=A, t=4:00 : 1
k=B
t=5:59
k=B
t=5:00
k=A
T=4:30
k=A
t=4:00

12. Scalability
• Large amount of data to process, arrival at high velocity
ᵒ Pipelining and partitioning
ᵒ Backpressure
• Partitioning
ᵒ Run same logic in multiple processes or threads
ᵒ Each partition processes a subset of the data
• Apex supports partitioning out of the box
ᵒ Different partitioning schemes
ᵒ Unification
ᵒ Static & Dynamic Partitioning
ᵒ Separation of processing logic from scaling decisions
12

13. Partitioning
13
NxM PartitionsUnifier
0 1 2 3
Logical DAG
0 1 2
1
1 Unifier
1
20
Logical Diagram
Physical Diagram with operator 1 with 3 partitions
0
Unifier
1a
1b
1c
2a
2b
Unifier 3
Physical DAG with (1a, 1b, 1c) and (2a, 2b): No bottleneck
Unifier
Unifier0
1a
1b
1c
2a
2b
Unifier 3
Physical DAG with (1a, 1b, 1c) and (2a, 2b): Bottleneck on intermediate Unifier

14. Advanced Partitioning
14
0
1a
1b
2 3 4Unifier
Physical DAG
0 4
3a2a1a
1b 2b 3b
Unifier
Physical DAG with Parallel Partition
Parallel Partition
Container
uopr
uopr1
uopr2
uopr3
uopr4
uopr1
uopr2
uopr3
uopr4
dopr
dopr
doprunifier
unifier
unifier
unifier
Container
Container
NICNIC
NICNIC
NIC
Container
NIC
Logical Plan
Execution Plan, for N = 4; M = 1
Execution Plan, for N = 4; M = 1, K = 2 with cascading unifiers
Cascading Unifiers
0 1 2 3 4
Logical DAG

15. Dynamic Partitioning
15
• Partitioning change while application is running
ᵒ Change number of partitions at runtime based on stats
ᵒ Determine initial number of partitions dynamically
• Kafka operators scale according to number of kafka partitions
ᵒ Supports re-distribution of state when number of partitions change
ᵒ API for custom scaler or partitioner
2b
2c
3
2a
2d
1b
1a1a 2a
1b 2b
3
1a 2b
1b 2c 3b
2a
2d
3a
Unifiers not shown

16. How dynamic partitioning works
16
• Partitioning decision (yes/no) by trigger (StatsListener)
ᵒ Pluggable component, can use any system or custom metric
ᵒ Externally driven partitioning example: KafkaInputOperator
• Stateful!
ᵒ Uses checkpointed state
ᵒ Ability to transfer state from old to new partitions (partitioner, customizable)
ᵒ Steps:
• Call partitioner
• Modify physical plan, rewrite checkpoints as needed
• Undeploy old partitions from execution layer
• Release/request container resources
• Deploy new partitions (from rewritten checkpoint)
ᵒ No loss of data (buffered)
ᵒ Incremental operation, partitions that don’t change continue processing
• API: Partitioner interface

17. Fault Tolerance
17
• Operator state is checkpointed to persistent store
ᵒ Automatically performed by engine, no additional coding needed
ᵒ Asynchronous and distributed
ᵒ In case of failure operators are restarted from checkpoint state
• Automatic detection and recovery of failed containers
ᵒ Heartbeat mechanism
ᵒ YARN process status notification
• Buffering to enable replay of data from recovered point
ᵒ Fast, incremental recovery, spike handling
• Application master state checkpointed
ᵒ Snapshot of physical (and logical) plan
ᵒ Execution layer change log

18. • In-memory PubSub
• Stores results emitted by operator until committed
• Handles backpressure / spillover to local disk
• Ordering, idempotency
Operator
1
Container 1
Buffer
Server
Node 1
Operator
2
Container 2
Node 2
Buffer Server
18

19. Recovery Scenario
… EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1
sum
0
… EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1
sum
7
… EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1
sum
10
… EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1
sum
7
19

20. Processing Guarantees
20
At-least-once
• On recovery data will be replayed from a previous checkpoint
ᵒ No messages lost
ᵒ Default, suitable for most applications
• Can be used to ensure data is written once to store
ᵒ Transactions with meta information, Rewinding output, Feedback from
external entity, Idempotent operations
At-most-once
• On recovery the latest data is made available to operator
ᵒ Useful in use cases where some data loss is acceptable and latest data is
sufficient
Exactly-once
ᵒ At-least-once + idempotency + transactional mechanisms (operator logic) to
achieve end-to-end exactly once behavior

21. End-to-End Exactly Once
21
• Becomes important when writing to external systems
• Data should not be duplicated or lost in the external system even in case of
application failures
• Common external systems
ᵒ Databases
ᵒ Files
ᵒ Message queues
• Platform support for at least once is a must so that no data is lost
• Data duplication must still be avoided when data is replayed from checkpoint
ᵒ Operators implement the logic dependent on the external system
ᵒ Platform provides checkpointing and repeatable windowing

22. Data Processing Pipeline Example
App Builder
22

23. Monitoring Console
Logical View
23

24. Monitoring Console
Physical View
24

25. Real-Time Dashboards
Real Time Visualization
25

26. Maximize Revenue w/ real-time insights
26
PubMatic is the leading marketing automation software company for publishers. Through real-time analytics,
yield management, and workflow automation, PubMatic enables publishers to make smarter inventory
decisions and improve revenue performance
Business Need Apex based Solution Client Outcome
• Ingest and analyze high volume clicks &
views in real-time to help customers
improve revenue
- 200K events/second data
flow
• Report critical metrics for campaign
monetization from auction and client
logs
- 22 TB/day data generated
• Handle ever increasing traffic with
efficient resource utilization
• Always-on ad network
• DataTorrent Enterprise platform,
powered by Apache Apex
• In-memory stream processing
• Comprehensive library of pre-built
operators including connectors
• Built-in fault tolerance
• Dynamically scalable
• Management UI & Data Visualization
console
• Helps PubMatic deliver ad performance
insights to publishers and advertisers in
real-time instead of 5+ hours
• Helps Publishers visualize campaign
performance and adjust ad inventory in
real-time to maximize their revenue
• Enables PubMatic reduce OPEX with
efficient compute resource utilization
• Built-in fault tolerance ensures
customers can always access ad
network

27. Industrial IoT applications
27
GE is dedicated to providing advanced IoT analytics solutions to thousands of customers who are using their
devices and sensors across different verticals. GE has built a sophisticated analytics platform, Predix, to help its
customers develop and execute Industrial IoT applications and gain real-time insights as well as actions.
Business Need Apex based Solution Client Outcome
• Ingest and analyze high-volume, high speed
data from thousands of devices, sensors
per customer in real-time without data loss
• Predictive analytics to reduce costly
maintenance and improve customer
service
• Unified monitoring of all connected sensors
and devices to minimize disruptions
• Fast application development cycle
• High scalability to meet changing business
and application workloads
• Ingestion application using DataTorrent
Enterprise platform
• Powered by Apache Apex
• In-memory stream processing
• Built-in fault tolerance
• Dynamic scalability
• Comprehensive library of pre-built
operators
• Management UI console
• Helps GE improve performance and lower
cost by enabling real-time Big Data
analytics
• Helps GE detect possible failures and
minimize unplanned downtimes with
centralized management & monitoring of
devices
• Enables faster innovation with short
application development cycle
• No data loss and 24x7 availability of
applications
• Helps GE adjust to scalability needs with
auto-scaling

28. Smart energy applications
28
Silver Spring Networks helps global utilities and cities connect, optimize, and manage smart energy and smart city
infrastructure. Silver Spring Networks receives data from over 22 million connected devices, conducts 2 million
remote operations per year
Business Need Apex based Solution Client Outcome
• Ingest high-volume, high speed data from
millions of devices & sensors in real-time
without data loss
• Make data accessible to applications
without delay to improve customer service
• Capture & analyze historical data to
understand & improve grid operations
• Reduce the cost, time, and pain of
integrating with 3rd party apps
• Centralized management of software &
operations
• DataTorrent Enterprise platform, powered
by Apache Apex
• In-memory stream processing
• Pre-built operator
• Built-in fault tolerance
• Dynamically scalable
• Management UI console
• Helps Silver Spring Networks ingest &
analyze data in real-time for effective load
management & customer service
• Helps Silver Spring Networks detect
possible failures and reduce outages with
centralized management & monitoring of
devices
• Enables fast application development for
faster time to market
• Helps Silver Spring Networks scale with
easy to partition operators
• Automatic recovery from failures

29. Demo / Q&A
29

30. Resources
30
• http://apex.apache.org/
• Learn more: http://apex.apache.org/docs.html
• Subscribe - http://apex.apache.org/community.html
• Download - http://apex.apache.org/downloads.html
• Follow @ApacheApex - https://twitter.com/apacheapex
• Meetups – http://www.meetup.com/pro/apacheapex/
• More examples: https://github.com/DataTorrent/examples
• Slideshare: http://www.slideshare.net/ApacheApex/presentations
• https://www.youtube.com/results?search_query=apache+apex
• Free Enterprise License for Startups -
https://www.datatorrent.com/product/startup-accelerator/