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Redis + Apache Spark =
Swiss Army Knife meets
Kitchen Sink
Yeshwanth Vijayakumar
Sr. Engineering Manager/Architect @ Adobe
Agenda
§ Niche 1
▪ Long Running Spark Batch
Job - Dispatch New Jobs by
polling a Redis Queue
§ Niche 2 :
▪ Distributed Counters
• Niche 1 :
Long Running Spark Batch Job
Problem Context
Run as many queries as possible in parallel on top a
denormalized dataframe
• foo = 1
Query 1
• bar.baz > 120
Query 2
• state in [CA, NY]
Query 3
Query 1000
ProfileIds field1 field1000 eventsArray
a@a.com a x [e1,2,3]
b@g.com b x [e1]
d@d.com d y [e1,2,3]
z@z.com z y [e1,2,3,5,7]
What do we need?
• Long Running Spark Batch Job
• Dispatch New Jobs by polling a Redis Queue
• We want to parametrize a Spark Action repeatedly for
interactive results
• E.g. Submit custom queries on top a table
• We load the data once query N times
• Bringing up a Spark Cluster per job has a latency cost
• Wasted time doing same initialization actions multiple times.
• Possible Multi tenancy
Why not Apache Livy et. al?
Why not Structured Streaming?
• Lack of access to Spark Context within executor context
• Can’t do a spark action on top of dataframe that is
already loaded in the driver unless you do a join
• Doing a join is extremely limited
Working Solution Summary
• Blocking POP on Redis inside driver and use Command
Pattern to send queries to rediscover queue
• Consume the commands and trigger spark actions using a
FAIR scheduler
• Communicate status of job through a micro
service/database or Redis itself!
Session Workflow – Spark
Continuous Session
10
Submit
Query API
Spark Driver
Executor 1
Executor N
Fetch
Results
Executor Logic
API
1. POST /preview
2. Check if result in Cache
1. GET /preview/<previewID> 2. Fetch Counters from Redis
3. Push <query> into queue
4. Pop queries till
queue is empty
[q1, q2, q3, q100]
Sample
Dataframe
Sample
Dataframe
partition
1
partition
2
partition
1
Working Solution – Code View
Working Solution – Code View
Working Solution – Code View
• Niche 2 :
Distributed Counters
What is wrong with Accumulators?
• Repeated Task Execution - Non idempotency
• Task Failures and Retries
• Re-using stage in repeated operations
• Speculative Execution
• Memory pressure on driver on collect()
• Can’t access per partition stats programmatically AFAIK
What is wrong with Accumulators? - Example
Utilize Redis Hashes as distributed counters
Utilize Redis Hashes as distributed counters
Excellent Throughput
Digging into Redis Pipelining + Spark
From https://redis.io/topics/pipelining
Without Pipelining With Pipelining
Important Config Optimizations
Off-Heap Allocation
Feedback
Your feedback is important to us.
Don’t forget to rate and review the sessions.

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Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

  • 1. Redis + Apache Spark = Swiss Army Knife meets Kitchen Sink Yeshwanth Vijayakumar Sr. Engineering Manager/Architect @ Adobe
  • 2. Agenda § Niche 1 ▪ Long Running Spark Batch Job - Dispatch New Jobs by polling a Redis Queue § Niche 2 : ▪ Distributed Counters
  • 3. • Niche 1 : Long Running Spark Batch Job
  • 5. Run as many queries as possible in parallel on top a denormalized dataframe • foo = 1 Query 1 • bar.baz > 120 Query 2 • state in [CA, NY] Query 3 Query 1000 ProfileIds field1 field1000 eventsArray a@a.com a x [e1,2,3] b@g.com b x [e1] d@d.com d y [e1,2,3] z@z.com z y [e1,2,3,5,7]
  • 6. What do we need? • Long Running Spark Batch Job • Dispatch New Jobs by polling a Redis Queue • We want to parametrize a Spark Action repeatedly for interactive results • E.g. Submit custom queries on top a table • We load the data once query N times • Bringing up a Spark Cluster per job has a latency cost • Wasted time doing same initialization actions multiple times. • Possible Multi tenancy
  • 7. Why not Apache Livy et. al?
  • 8. Why not Structured Streaming? • Lack of access to Spark Context within executor context • Can’t do a spark action on top of dataframe that is already loaded in the driver unless you do a join • Doing a join is extremely limited
  • 9. Working Solution Summary • Blocking POP on Redis inside driver and use Command Pattern to send queries to rediscover queue • Consume the commands and trigger spark actions using a FAIR scheduler • Communicate status of job through a micro service/database or Redis itself!
  • 10. Session Workflow – Spark Continuous Session 10 Submit Query API Spark Driver Executor 1 Executor N Fetch Results Executor Logic API 1. POST /preview 2. Check if result in Cache 1. GET /preview/<previewID> 2. Fetch Counters from Redis 3. Push <query> into queue 4. Pop queries till queue is empty [q1, q2, q3, q100] Sample Dataframe Sample Dataframe partition 1 partition 2 partition 1
  • 11. Working Solution – Code View
  • 12. Working Solution – Code View
  • 13. Working Solution – Code View
  • 14. • Niche 2 : Distributed Counters
  • 15. What is wrong with Accumulators? • Repeated Task Execution - Non idempotency • Task Failures and Retries • Re-using stage in repeated operations • Speculative Execution • Memory pressure on driver on collect() • Can’t access per partition stats programmatically AFAIK
  • 16. What is wrong with Accumulators? - Example
  • 17. Utilize Redis Hashes as distributed counters
  • 18. Utilize Redis Hashes as distributed counters
  • 20. Digging into Redis Pipelining + Spark From https://redis.io/topics/pipelining Without Pipelining With Pipelining
  • 21.
  • 23. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.