advanced visualization of spark jobs

Advanced visualization of Spark jobs

Zoltán Zvarazoltan.zvara@sztaki.hu

Márton Balassimarton.balassi@sztaki.hu

Agenda• Introduction• Motivation and the challenges• The Spark execution visualization• An elegant way to tackle data skew• The visualizer extended to Flink• Future plans

Introduction• Hungarian Academy of Sciences (MTA SZTAKI)• Research institute with strong industry ties• Big Data projects using Spark, Flink, Cassandra, Hadoop etc.• Multiple telco use cases lately, with challenging data volume and

distribution

Motivation• We have developped an application aggreagting telco data that tested

well on toy data• When deploying it against the real dataset the application seemed

healthy• However it could become suprisingly slow or even crash• What did go wrong?

Data skew• When some data-points are very frequent, and we need to process

them on a single machine – aka the Bieber-effect• Power-law distributions are very common: from social-media to

telecommunication, even in our beloved WordCount• Default hashing puts these into „random” buckets• We can do better than that

Aim1. Most of the telco data-processing workloads suffer from inefficient

communication patterns, introduced by skewed data2. Help developers to write better applications by detecting issues of

logical and physical execution plans easier3. Help newcomers to understand a distributed data-processing

system faster4. Demonstrate and guide the testing of adaptive (re)partitioning

techniques

Spark’s execution model• Extended MapReduce model, where a previous stage has to complete

all its tasks before the next stage can be scheduled (global synchronization)• Data skew can introduce slow tasks• In most cases, the data distribution

is not known in advance• „Concept drifts” are common

stageboundary & shuffle

even partitioning skewed data

slow task

slow task

Physical execution plan• Understanding the physical plan• can lead to designing a better application• can uncover bottlenecks that are hard to identify otherwise• can help to pick the right tool for the job at hand• can be quite difficult for newcomers• can give insights to new, adaptive, on-the-fly partitioning & optimization

strategies

Current visualizations in Spark• Current visualization tools• are missing the fine-grained input & output metrics – also not available in

most sytems, like Spark• does not capture the data charateristics – hard to accomplish a lightweight

and efficient solution• does not visualize the physical plan

DAG visualization in Spark

Collecting information during execution• Data-transfer between tasks are accomplished in

the shuffle phase, in the form of shuffle block fetches• We distinguish local & remote block fetches

• Data characteristics collected on shuffle write & read

A scalable sampling• Key-distributions are approximated with a strategy, that• is not sensitive to early or late concept drifts,• lightweight and efficient,• scalable by using a backoff strategy

keys

freq

uenc

y

counters

keys

freq

uenc

ysampling rate of sampling rate of

keys

freq

uenc

y

sampling rate of

truncateincrease by

𝑇𝐵

Availability through the Spark REST API• Enhanced Spark REST API to provide block fetch & data characteristics

information of tasks• New queries to the REST API, for example: „what happend in the last

3 seconds?”• /api/v1/applications/{appId}/{jobId}/tasks/{timestamp}

THE VISUALIZATION

Our way of tackling data skew• Goal: use the collected information to handle data skew dynamically,

on-the-fly on any workload (batch or streaming)• Currently paper in progress

heavy keys createheavy partitions (slow tasks)

Bieber-key is alone,size of the biggest partition

is minimized

Making visualization pluggable• Let us also support Flink• The execution model is quite different from Spark• Minimal code change on the data reader module of the visualization• Expose the necessary metrics through the Flink JM Rest API• Map Flink’s model to Spark’s

Computational models

Batch data

Kafka, RabbitMQ ...

HDFS, JDBC ...

Stream Data

Spark RDDs break down the computation into stagesFlink computation is fully pipelined by default

The currently available Flink visualizer

Granular visualization of Flink jobs

Future plans• Open-sourcing of the visualization framework• Dynamic repartitioning paper is in progress• Task-classification to make the visualization more compact• Visual improvements, more features• Opening PRs against Spark & Flink with the suggested metrics• A rewrite is coming for the Flink metrics, track it at FLINK-1504

Conclusions• The devil is in the details• Visualizations can aid developers to better understand issues and

bottlenecks of certain workloads• Specially data skew can hinder the completion time of the whole

stage in a batch engine• Adaptive repartitioning strategies can be suprisingly efficient

Thank you for your attentionQ&A

stageboundary & shuffle

even partitioning skewed data

slow task

slow task