This presentation will introduce usage of Apache Apex for Time Series & Data Ingestion Service by General Electric Internet of things Predix platform. Apache Apex is a native Hadoop data in motion platform that is being used by customers for both streaming as well as batch processing. Common use cases include ingestion into Hadoop, streaming analytics, ETL, database off-loads, alerts and monitoring, machine model scoring, etc. Abstract: Predix is an General Electric platform for Internet of Things. It helps users develop applications that connect industrial machines with people through data and analytics for better business outcomes. Predix offers a catalog of services that provide core capabilities required by industrial internet applications. We will deep dive into Predix Time Series and Data Ingestion services leveraging fast, scalable, highly performant, and fault tolerant capabilities of Apache Apex. Speakers: - Venkatesh Sivasubramanian, Sr Staff Software Engineer, GE Predix & Committer of Apache Apex - Pramod Immaneni, PPMC member of Apache Apex, and DataTorrent Architect

1. Predix Time Series
with Apache Apex

2. Hello!
Venkat
Predix Data Services,
GE Digital
Big Data & Analytics
@WalmartLabs.
Pramod
Senior Architect,
DataTorrent Inc,
Apex PPMC Member

3. Quick
Survey

4. ▪ Predix Platform Overview
▪ Predix Time Series
▪ Apache Apex
▪ Stream Processing with Apex – Journey and Learning
▪ Demo
▪ Q & A
Outline

5. ▪ Platform for Industrial Internet
▪ Based on Cloud Foundry
▪ Provides rich set of services for rapid development
▪ Managed and Secured infrastructure
▪ Marketplace for Services
Predix Platform

6. Want
big
impact?
Use big
image.

7. Predix Platform
Architecture

8. Who we are?Team Data Services
Love Java and Go
Distributed Systems
Big & Fast Data
We are Hiring!

9. Predix Time Series
Overview
▪ Streaming Ingestion
▪ Efficient storage
▪ Indexing the data for quick retrieval.
▪ Guaranteed data processing
▪ Highly available and scalable.
▪ Millisecond data point precision
▪ Support for String and Numbers
▪ Secured Access

10. Predix Time Series
Architecture

11. ▪ Support Interpolation
▪ Aggregations (percent, avg, sum, count)
▪ Filter by Attributes, Quality and Value
▪ Support for Limit and Order By
▪ Both GET and POST to retrieve data points
▪ Sub-second query performance
Predix Time Series
API Sample
{
"tags": [
{
"name": ["WIND_SPEED"],
"filters": {
"attributes": {
"farm":["CA"]
}
},
"limit": 1000,
"groups": {
}
}
]
}

12. ▪ Signup @ Predix.io
▪ Create Time Series Instance
▪ Bind to an application
▪ Get credentials and connect your device
▪ Query the data
Predix Time Series
Get Started?

13. Apache Apex
Overview
▪Streaming Analytics Platform
▪Event based, low latency
▪Scalable and Highly available
▪Managed State
▪Library of pre-built operators

14. Apex Platform

15. Stream Processing
Events
Reader
Filter
Operator
Filter
Operator
“Top K”
Operator
“Top K”
Operator
Datastore
Writer
Partition
Stream
Unify
Stream
DAG
Local/
Remote
Find Top K engines with High/Low Oil pressure

16. Windowing Support
 Application
window
 Sliding/Tumb
ling Window
 Checkpoint
window
 No artificial
latency

17. Application Specification

18. Why Apache Apex
Development
High Performance and
Distributed
Dynamic Partitions
Rich set of operator
library
Support for atleast-once,
atmost-once and exactly-
once processing
semantics
Operations
Hadoop/Yarn Compatibility
Fault tolerance and
Platform Stability
Ease of deployment and
operability
Enterprise grade security

19. Time Series
DAG
Skimmed Version of
the DAG

20. Partitioning
Strategies

21. Input
Operator
Detection
Operator
Output
Operator
Logical DAG
Detection
Operator
Input
Operator
Detection
Operator
Unifier
Operator
Detection
Operator`
Output
Operator
Physical DAG

22. ▪ Utilize hashcode and mask to determine Partition
▪ Mask picks the last n bits of the hashcode of the tuple
▪ StreamCodec can be used to specify custom hashcode
▪ Custom partitioner can be used to change default map
Stream Split
tuple:{
Sensor,
98871231, 34,
GOOD
}
Hashcode:
0010101000101
01
Mask (0x11) Partition
00 1
01 2
10 3
11 4

23. MxN Partitioning
Input
Operator
Detection
Operator
Detection
Operator
Output
Operator
Output
Operator
Input
Operator
Input
Operator
Detection
Operator
Output
Operator
Detection
Operator
 Default Mechanism
 StatelessPartitioner
<property>
<name>dt.application.<streamingApp>.operator.<name>.attr.PARTITIONER</name>
<value>com.datatorrent.common.partitioner.StatelessPartitioner:4</value>
</property>

24. Parallel Partitioning
Input
Operator
Detection
Operator
Detection
Operator
Output
Operator
Output
Operator
Input
Operator
Input
Operator
Detection
Operator
Output
Operator
<property>
<name>dt.application.<streamApp>.operator.<name>.port.input.attr.PARTITION_PARALLEL</name>
<value>true</value>
</property>

25. Unifier
▪ Combines outputs of multiple partitions
▪ Runs as an operator
▪ Logic depends on the operator functionality
▸Example if operator is computing average, unifier is computing final
average from individual average and counts
▪ Default unifier if none specified
▪ Helps with skew
▪ Cascading unification possible if unification needs to be done in multiple
stages

26. Custom partitioning
▪ Custom stream splitting
▪ Distribution of state during initial or dynamic partitioning
 Kafka operators scale according to number of kafka partitions
 Re-distribution of state during dynamic partitioning
tuple:{
Sensor,
98871231, 34,
GOOD
}
Hashcode:
0010101000101
01
Mask (0x00) Partition
00 1
00 2
00 3
00 4

27. Time Series DAG

28. Check pointing is tied to
the application id. This
problem becomes pertinent
if you are relying on that
state to do further
processing.
Solution
Store states that matter
externally, eg. HDFS,
Zookeeper, Redis.
Problems
Encountered

29. Kafka Source was moving
an offset as committed as
soon as it read. Becomes
a problem when the
message is not completely
processed by the DAG
Solution
Kafka Source was modified
to wait till the messages are
entirely processed in the
DAG. Thanks to the
community! We also
implemented an offset
manager and stored the
offset in ZK
Problems
Encountered

30. Gracefully stopping DAG
during upgrade, to get
exactly once semantics,
when downstream systems
cannot handle duplicates or
support transactions
Solution
Added a property to Mute
the Source Operators and
drain the messages before
you bring the streaming
pipeline down. APIs
available for automation.
Problems
Encountered

31. Event time based
processing and out of order
data arrival
Solution
We have built some
Spooling Data structures
working with the apex team.
Working to open source this.
Problems
Encountered

32. Key Takeaways
▪ Upgradeability and tolerance for failure
▪ Monitoring DAG for failures
▪ Static partitioning helps only so much
▪ Continuous Integration and Deployment
▪ Performance Testing and Benchmarking
▪ Ship and Store logs

33. Fault Tolerance

34. Fault tolerance
▪ Operator state is checkpointed to a persistent store
▸ Automatically performed by engine, no additional work needed by
operator
▸ In case of failure operators are restarted from checkpoint state
▸ Frequency configurable per operator
▸ Asynchronous and distributed by default
▸ Default store is HDFS
▪ Automatic detection and recovery of failed operators
▸ Heartbeat mechanism
▪ Buffering mechanism to ensure replay of data from recovered point
so that there is no loss of data
▪ Application master state checkpointed

35. Message Processing Semantics
Atleast once [1..n]
▪ On recovery operator state is restored to a checkpoint
▪ Data is replayed from the checkpoint so it is effectively a rewind
▪ Messages will not be lost
▪ Default mechanism and is suitable for most applications
▪ End-to-end exactly once i.e., data is written only once to store in
case of fault recovery
▸ Idempotent operations
▸ Rewinding output
▸ Writing meta information to store in transactional fashion
▸ Feedback from external store on last processed message

36. Message Processing Semantics
Atmost once [0,1]
▪ On recovery the latest data is made available to operator
▪ Useful in use cases where some data loss is acceptable and latest
data is sufficient
Windowed exactly once [0,1]
▪ Operators checkpointed every window
▪ Can be combined with transactional mechanisms to ensure end-to-
end exactly once behavior

37. Stream Locality
▪ By default operators are deployed in containers (processes)
randomly on different nodes across the hadoop cluster
▪ Custom locality for streams
▸ Rack local: Data does not traverse network switches
▸ Node local: Data is passed via loopback interface and frees up
network bandwidth
▸ Container local: Messages are passed via in memory queues
between operators and does not require serialization
▸ Thread local: Messages are passed between operators in a
same thread equivalent to calling a subsequent function on the
message

38. What happens during launch?
▪ User launches an application using the management
console or command line client
▪ DAG gets assembled on the client
▪ DAG and dependency jars gets saves to HDFS
▪ App Master (StrAM) gets launched on a Hadoop node
▸Converts logical plan to physical plan
▸Figures out execution plan
▸Requests containers from Hadoop
▸Launches StreamingContainer in individual
containers with relevant operators

39. Kafka Operator
▪ Supports both High and Low Level API Implementation
▪ Finer level control of offset for Exactly-Once Semantics
▪ Supports ONE_TO_ONE and ONE_TO_MANY Partition Strategy
▪ Consume by size and number of messages
▪ Fault tolerent to recover from offsets

40. Debugging Issues
▪ Distributed systems are hard to debug
▪ LocalMode comes handy for developer testing and debugging
▪ Enable Yarn log aggregation
▸ yarn logs –applicationID <App_Id>
▪ DataTorrent webconsole provides streaming access to AppMaster and
Container logs
▪ Understanding what happens where
▸ AppMaster
▸ NodeManager
▸ Containers

41. Demo

45. Thanks
!!
Any questions?
You can find us at @venkyz and @pramod

46. Bulk Upload - DAG
Rule Based Alerting - DAG