Spark Operator—Deploy, Manage and Monitor Spark clusters on Kubernetes

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The document discusses the Spark Operator, which allows deploying, managing, and monitoring Spark clusters on Kubernetes. It describes how the operator extends Kubernetes by defining custom resources and reacting to events from those resources, such as SparkCluster, SparkApplication, and SparkHistoryServer. The operator takes care of common tasks to simplify running Spark on Kubernetes and hides the complexity through an abstract operator library.

Data & Analytics

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Deployment
StatefulSet
Job
Pod
Service
ReplicationController

Manifest Nightmares
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Operator Pattern
• Extends Kubernetes
• Resources and Controllers
• Custom Resource Definitions (CRD)
• Reacts on events when resource is CRUDed
• Sometimes referred as Custom Controllers
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Operator<X> - example
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Operator K8s API
I am listening on CR<X>
CR<X> …. CustomResource representing the desired configuration of X

Operator<X> - example
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Operator K8s API
OK, whatever
¯_( ツ )_/¯

Operator<X> - example
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Operator K8s API
Hey! New resource

Operator<X> - example
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Operator K8s API
Beep!Beep!
Boop!Zzzz!
⚡⚡

Comparison
Operator can be seen merely as deployment
mechanism, but it can do much more
• Kubernetes manifests
• Helm Chart
• Ansible
• Kustomize
• Ksonnet
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$Operator minimal example namespace=${WATCH_NAMESPACE:-default} base=http://localhost:8001 ns=namespaces/$namespace curl -N -s $base/api/v1/${ns}/configmaps?watch=true | while read -r event do # ... done 12#UnifiedDataAnalytics #SparkAISummit$

Spark Operator
• Started as toy project
• Adopted by AI-CoE project OpenDataHub.io
• Compatible with Spark operator from Google to
avoid vendor lock-in
• Available also in operatorhub.io or Helm chart
or using ansible role
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Spark Operator
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Reacts on events from these custom resources:
• SparkCluster
• SparkApplication
• SparkHistoryServer

Spark Operator
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Reacts on events from these custom resources:
• SparkCluster
• SparkApplication
• SparkHistoryServer
Full schema captured by JSON schema

Spark Operator
18#UnifiedDataAnalytics #SparkAISummit
Reacts on events from these custom resources:
• SparkCluster
• SparkApplication
• SparkHistoryServer

Fabric8 Kubernetes client
Fluent API
Type-safety
Takes the credentials from:
• kube config file
• service account token & mounted CA cert
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Abstract Operator Library
• Automates the common tasks
• User has to only extend the class and override
couple of methods.
• Supports JSON schema as the representation
of the configuration.
• CRDs and CMs supported
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Dependencies
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operator-parent-pom
spark-operator abstract-operator kubernetes-client
depends on
has parent

Tooling
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• Soit – Python CLI that verifies if container
image is “operator compliant”
• Ansible role – it supports also deploying
Prometheus together with the operator
• Oshinko-temaki – CLI that produces valid
yamls with custom resources for the operator
All the tools are available in the readme file

Metrics
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• Endpoints for Prometheus
• Operator metrics (including JVM metrics)
• Metrics from deployed Spark clusters

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22

Takeaways
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• Spark on K8s can be easy
• Operator can hide complexity
• Operators can be done in any language
• Hopefully in Spark:
http://bit.ly/spark-op-pr

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In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs. There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs. The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.

Stage Level Scheduling Improving Big Data and AI Integration

Databricks

In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks. Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model? The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity. The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters. In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Databricks

There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal. In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.

Scaling your Data Pipelines with Apache Spark on Kubernetes

Databricks

Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Databricks

In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.

Sawtooth Windows for Feature Aggregations

Databricks

We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark. Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue · Why? o Custom queries on top a table; We load the data once and query N times · Why not Structured Streaming · Working Solution using Redis Niche 2 : Distributed Counters · Problems with Spark Accumulators · Utilize Redis Hashes as distributed counters · Precautions for retries and speculative execution · Pipelining to improve performance

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Databricks

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Re-imagine Data Monitoring with whylogs and Spark

Databricks

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Raven: End-to-end Optimization of ML Prediction Queries

Databricks

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Databricks

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Massive Data Processing in Adobe Using Delta Lake

Databricks

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Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

Raven: End-to-end Optimization of ML Prediction Queries

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

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Spark Operator—Deploy, Manage and Monitor Spark clusters on Kubernetes

1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2. Jiri Kremser, Red Hat Spark Operator Deploy, Manage and Monitor Spark clusters on Kubernetes #UnifiedDataAnalytics #SparkAISummit

3. 3#UnifiedDataAnalytics #SparkAISummit

4. 4#UnifiedDataAnalytics #SparkAISummit Deployment StatefulSet Job Pod Service ReplicationController

5. Manifest Nightmares 5#UnifiedDataAnalytics #SparkAISummit

6. Operator Pattern • Extends Kubernetes • Resources and Controllers • Custom Resource Definitions (CRD) • Reacts on events when resource is CRUDed • Sometimes referred as Custom Controllers 6#UnifiedDataAnalytics #SparkAISummit

7. Operator<X> - example 7#UnifiedDataAnalytics #SparkAISummit Operator K8s API I am listening on CR<X> CR<X> …. CustomResource representing the desired configuration of X

8. Operator<X> - example 8#UnifiedDataAnalytics #SparkAISummit Operator K8s API OK, whatever ¯_( ツ )_/¯

9. Operator<X> - example 9#UnifiedDataAnalytics #SparkAISummit Operator K8s API Hey! New resource

10. Operator<X> - example 10#UnifiedDataAnalytics #SparkAISummit Operator K8s API Beep!Beep! Boop!Zzzz! ⚡⚡

11. Comparison Operator can be seen merely as deployment mechanism, but it can do much more • Kubernetes manifests • Helm Chart • Ansible • Kustomize • Ksonnet 11#UnifiedDataAnalytics #SparkAISummit

12. Operator minimal example namespace=${WATCH_NAMESPACE:-default} base=http://localhost:8001 ns=namespaces/$namespace curl -N -s $base/api/v1/${ns}/configmaps?watch=true | while read -r event do # ... done 12#UnifiedDataAnalytics #SparkAISummit

13. Spark Operator • Started as toy project • Adopted by AI-CoE project OpenDataHub.io • Compatible with Spark operator from Google to avoid vendor lock-in • Available also in operatorhub.io or Helm chart or using ansible role 13#UnifiedDataAnalytics #SparkAISummit

14. Spark Operator 14#UnifiedDataAnalytics #SparkAISummit Reacts on events from these custom resources: • SparkCluster • SparkApplication • SparkHistoryServer

15. Spark Operator 15#UnifiedDataAnalytics #SparkAISummit Reacts on events from these custom resources: • SparkCluster • SparkApplication • SparkHistoryServer Full schema captured by JSON schema

16. 16#UnifiedDataAnalytics #SparkAISummit

17. 17#UnifiedDataAnalytics #SparkAISummit

18. Spark Operator 18#UnifiedDataAnalytics #SparkAISummit Reacts on events from these custom resources: • SparkCluster • SparkApplication • SparkHistoryServer

19. Fabric8 Kubernetes client Fluent API Type-safety Takes the credentials from: • kube config file • service account token & mounted CA cert 19#UnifiedDataAnalytics #SparkAISummit

20. Abstract Operator Library • Automates the common tasks • User has to only extend the class and override couple of methods. • Supports JSON schema as the representation of the configuration. • CRDs and CMs supported 20#UnifiedDataAnalytics #SparkAISummit

21. Dependencies 21#UnifiedDataAnalytics #SparkAISummit operator-parent-pom spark-operator abstract-operator kubernetes-client depends on has parent

22. Tooling 22#UnifiedDataAnalytics #SparkAISummit • Soit – Python CLI that verifies if container image is “operator compliant” • Ansible role – it supports also deploying Prometheus together with the operator • Oshinko-temaki – CLI that produces valid yamls with custom resources for the operator All the tools are available in the readme file

23. Metrics 23#UnifiedDataAnalytics #SparkAISummit • Endpoints for Prometheus • Operator metrics (including JVM metrics) • Metrics from deployed Spark clusters

24. 24#UnifiedDataAnalytics #SparkAISummit 22

25. Takeaways 25#UnifiedDataAnalytics #SparkAISummit • Spark on K8s can be easy • Operator can hide complexity • Operators can be done in any language • Hopefully in Spark: http://bit.ly/spark-op-pr

26. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT

Spark Operator—Deploy, Manage and Monitor Spark clusters on Kubernetes

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