Apache Spark and Online Analytics
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Sudarshan Kadambi presented this talk at the Bay Area Spark Meetup @ Bloomberg. He covered Bloomberg Apache Spark Server and contributions to Apache Spark. The talk also talked about challenges of doing high-volume online analytics while still observing high-levels of SLAs
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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.
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorchDatabricks 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.
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks 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 and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark PipelinesDatabricks 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.
Sawtooth Windows for Feature Aggregations
Sawtooth Windows for Feature AggregationsDatabricks 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.
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen SinkDatabricks 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
Re-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and SparkDatabricks 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.
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction QueriesDatabricks 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.
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkDatabricks 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.
Massive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta LakeDatabricks 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!
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Apache Spark and Online Analytics
- 1. Spark and Online Analytics Sudarshan Kadambi Copyright 2016 Bloomberg L.P. All rights reserved.
- 2. Agenda • Data and Analytics at Bloomberg • The role of Spark • The Bloomberg Spark Server • Spark for Online use-cases
- 3. Data and Analytics is our Business
- 4. Analytics at Bloomberg • Human-time, interactive analytics • Scalability – Handle increasingly sophisticated client analytic workflows – Ad-hoc and cross-domain aggregations, filtering • Heterogeneous data stores – Analytics often requires data from multiple stores • Low-latency updates, in addition to queries
- 5. Spark for Bloomberg Analytics • Distributed compute scales well for – large security universes and – multi-universe cross-domain queries • Abstract away heterogeneous data sources and present consistent interface for efficient data access – Spark as a tool for systems integration • Connectors and primitives to deal with incoming streams • Cache intermediate compute for fast queries
- 6. Spark as a Service? 6 • Stand-alone Spark Apps on isolated clusters pose challenges: – Redundancy in: » Crafting and Managing of RDDs/DFs » Coding of the same or similar types of transforms/actions – Management of clusters, replication of data, etc. – Analytics are confined to specific content sets making Cross-Asset Analytics much harder – Need to handle Real-time ingestion in each App Spark Cluster Spark App Spark Cluster Spark Server Spark App Spark App Spark Cluster Spark App
- 7. Bloomberg Spark Server Spark Context Request Processor Request Processor Request Processor Request Handler MDF Registry 7 Function Transform Registry (FTR) RSI … use Ingestion Manager MDF1 MDF2 1 2 1 2
- 8. Spark Server: Content Caching • Data access has long tail characteristics • High value data sub-setted within Spark • Specified as a filter predicate at time of registration • Seamless unification of data in Spark and backing store
- 9. Spark HA: State of the World – Execution lineage in Driver • Recovery from lost RDDs – RDD Replication • Low latency, even with lost executors • Support for “MEMORY_ONLY”, “MEMORY_ONLY_2”, “MEMORY_ONLY_SER”, “MEMORY_ONLY_SER_2” modes for in-memory persistence. Easily extensible to more replicas if needed. – Speculative execution • Minimizing performance hit from stragglers – Off-heap data • Minimizing GC stalls
- 10. Spark Architecture RPC Environment RPC Environment BlockManagerMasterEndpoint BlockManagerMaster Driver Executor - 2 BlockManager RPC Environment BlockManagerMaster Executor - 1 BlockManager RDD - 0 RDDBlock(0, Partition-1) RDDBlock(0, Partition-2)
- 11. RDD Block Replication Executor-1 Executor-2Driver Compute RDD Computation complete Get Peers for replication List of Peers Replicate block to Peer Block stored locallyResults of computation
- 12. RDD Block Replication: Challenges – Lost RDD partitions costly to recover • Data replenished at query time – RDD replicated to random executors • On YARN, multiple executors can be brought up on the same node in different containers • Hence multiple replicas possible on the same node/rack, susceptible to node/rack failure • Lost block replicas not recovered proactively
- 13. Topology Aware Replication (SPARK-15352) – Ideas & Implementation by Shubham Chopra – Making Peer selection for replication pluggable • Driver gets topology information for executors • Executors informed about this topology information • Executors use prioritization logic to order peers for block replication • Pluggable TopologyMapper and BlockReplicationPrioritizer • Default implementation replicates current Spark behavior
- 14. Topology Aware Replication (SPARK-15352) – Customizable prioritization strategies to suit different deployments • Variety of replication objectives – ReplicateToDifferentHost, ReplicateBlockWithinRack, ReplicateBlockOutsideRack • Optimizer to find a minimum number of peers to meet the objectives • Replicate to these peers with a higher priority – Proactive replenishment of lost replicas • BlockManagerMasterEndpoint triggered replenishment when an executor failure is detected.
- 15. Spark HA: Challenges – High Availability of Spark Driver • High bootstrap cost to reconstructing cluster and cached state • Naïve HA models (such as multiple active clusters) surface query inconsistency – High Availability and Low Tail Latency closely related
- 16. Spark HA – A Strawman • Multiple Spark Servers in Leader-Standby configuration • Each Spark Server backed by a different Spark Cluster • Each Spark Server refreshed with up-to- date data • Queries to standbys redirected to leader • Only leader responds to queries - Data consistency • RDD Partition loss in the leader still a concern • Performance still gated by slowest executor in leader • Resource usage amplified by the number of Spark Servers
- 17. Spark Driver State • Spark Driver is an arbitrary Java application • Only a subset of the state is interesting or expensive to reconstruct • For online-use cases, only RDDs/DFs created during ingestion are of interest • Expressing ingestion using DFs has better decoupling of data/state than RDDs
- 18. Spark Driver State* • BlockManagerMasterEndpoint holds Block<->Executor assignment • Cache Manager holds Logical Plan and DataFrame references – Used to short-circuit queries with pre-cached query plans, if possible • Job Scheduler – Keeps a track of various stages and tasks being scheduled • Executor information – Hostname and ports of live executors *Illustrative, not exhaustive
- 19. Externalizing Driver State Benefits: – Quicker recoveries – No need to restart executors – State accessible from multiple Active-Active drivers Solutions: – Off-heap storage for RDDs – Residual book-keeping driver state externalized to ZooKeeper