Components of an Apex application running on YARN, how they are made fault tolerant, how checkpointing works, recovery from failures, incremental recovery, processing guarantees.

1. Apache Apex
Fault Tolerance and Processing Semantics
San Francisco Apache Apex Meetup
April 6th 2016
Thomas Weise, Apache Apex PPMC
@thweise thw@apache.org

2. Stream Processing
• Paradigm shift in big data processing
• Data from a variety of sources (Kafka, files, social media etc.)
• Unbounded data sources
• A batch can be processed as stream (but a stream is not a batch)
• Processing with temporal boundaries (windows)
• Results stored to a 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. Apache Apex Features
• In-memory Stream Processing
• Scale out, Distributed, Parallel, High Throughput
• Windowing (temporal boundary)
• Reliability, Fault Tolerance
• Operability
• YARN native
• Compute Locality
• Dynamic updates
3

4. Apex Stack Overview
4

5. Native Hadoop Integration
5
• YARN is
the
resource
manager
• HDFS used
for storing
any
persistent
state

6. Recovery Demo
6

7. Streaming Windows
7
 Application window
 Sliding window and tumbling window
 Checkpoint window
 No artificial latency

8. Fault Tolerance
8
• 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

9. Checkpointing Operator State
9
• Save state of operator so that it can be recovered on failure
• Pluggable storage handler
• Default implementation
ᵒ Serialization with Kryo
ᵒ All non-transient fields serialized
ᵒ Serialized state written to HDFS
ᵒ Writes asynchronous, non-blocking
• Possible to implement custom handlers for alternative approach to
extract state or different storage backend (such as IMDG)
• For operators that rely on previous state for computation
ᵒ Operators can be marked @Stateless to skip checkpointing
• Checkpoint frequency tunable (by default 30s)
ᵒ Based on streaming windows for consistent state

10. • 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
10

11. Application Master State
11
• Snapshot state on plan change
ᵒ Serialize Physical Plan (includes logical plan)
ᵒ Infrequent, expensive operation
• WAL (Write-ahead-Log) for state changes
ᵒ Execution layer changes
ᵒ Container, operator state, property changes
• Containers locate master through DFS
ᵒ AM can fail and restart, other containers need to find it
ᵒ Work preserving restart
• Recovery
ᵒ YARN restarts application master
ᵒ Apex restores state from snapshot and replays log

12. • Container process fails
• NM detects
• In case of AM (Apex Application Master), YARN launches replacement
container (for attempt count < max)
• Node Manager Process fails
• RM detects NM failure and notifies AM
• Machine fails
• RM detects NM/AM failure and recovers or notifies AM
• RM fails - RM HA option
• Entire YARN cluster down – stateful restart of Apex application
Failure Scenarios
12

13. NM NM
Resource
Manager
Apex AM
3
2
1
Apex AM
1 2
3
NM
Failure Scenarios
NM
13

14. Failure Scenarios
… 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
14

15. Processing Guarantees
15
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

16. End-to-End Exactly Once
16
• 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
• Aid of platform features such as stateful checkpointing and windowing
• Three different mechanisms with implementations explained in next slides

17. Files
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• Streaming data is being written to file on a continuous basis
• Failure at a random point results in file with an unknown amount of data
• Operator works with platform to ensure exactly once
ᵒ Platform responsibility
• Restores state and restarts operator from an earlier checkpoint
• Platform replays data from the exact point after checkpoint
ᵒ Operator responsibility
• Replayed data doesn’t get duplicated in the file
• Accomplishes by keeping track of file offset as state
ᵒ Details in next slide
• Implemented in operator AbstractFileOutputOperator in apache/incubator-
apex-malhar github repository available here
• Example application AtomicFileOutputApp available here

18. Exactly Once Strategy
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File Data
Offset
• Operator saves file offset during
checkpoint
• File contents are flushed before
checkpoint to ensure there is no
pending data in buffer
• On recovery platform restores the file
offset value from checkpoint
• Operator truncates the file to the
offset
• Starts writing data again
• Ensures no data is duplicated or lost
Chk

19. Transactional databases
19
• Use of streaming windows
• For exactly once in failure scenarios
ᵒ Operator uses transactions
ᵒ Stores window id in a separate table in the database
ᵒ Details in next slide
• Implemented in operator AbstractJdbcTransactionableOutputOperator in
apache/incubator-apex-malhar github repository available here
• Example application streaming data in from kafka and writing to a JDBC
database is available here

20. Exactly Once Strategy
20
d11 d12 d13
d21 d22 d23
lwn1 lwn2 lwn3
op-id wn
chk wn wn+1
Lwn+11 Lwn+12 Lwn+13
op-id wn+1
Data Table
Meta Table
• Data in a window is written out in a single
transaction
• Window id is also written to a meta table
as part of the same transaction
• Operator reads the window id from meta
table on recovery
• Ignores data for windows less than the
recovered window id and writes new data
• Partial window data before failure will not
appear in data table as transaction was not
committed
• Assumes idempotency for replay

21. Stateful Message Queue
21
• Data is being sent to a stateful message queue like Apache Kafka
• On failure data already sent to message queue should not be re-sent
• Exactly once strategy
ᵒ Sends a key along with data that is monotonically increasing
ᵒ On recovery operator asks the message queue for the last sent message
• Gets the recovery key from the message
ᵒ Ignores all replayed data with key that is less than or equal to the recovered key
ᵒ If the key is not monotonically increasing then data can be sorted on the key at the end
of the window and sent to message queue
• Implemented in operator AbstractExactlyOnceKafkaOutputOperator in
apache/incubator-apex-malhar github repository available here

22. Resources
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• Exactly-once processing: https://www.datatorrent.com/blog/end-to-end-
exactly-once-with-apache-apex/
• Examples with Kafka and Files: https://github.com/tweise/apex-
samples/tree/master/exactly-once
• Learn more: http://apex.incubator.apache.org/docs.html
• Subscribe - http://apex.incubator.apache.org/community.html
• Download - http://apex.incubator.apache.org/downloads.html
• Apex website - http://apex.incubator.apache.org/
• Follow @ApacheApex - https://twitter.com/apacheapex
• Meetups - http://www.meetup.com/topics/apache-apex

23. Q&A
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