Degrading Performance? You Might be Suffering From the Small Files Syndrome

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Small file sizes can degrade performance in Spark and Hive queries. This is because each small file requires overhead to open, read, and process. The problem is common with event streaming data and IoT sensors that produce many small files. To detect the issue, check for data skew across partitions and Spark job writers processing many small files. Mitigation techniques include file hierarchy designs, repartitioning, Delta Lake optimizations, and Databricks Auto Optimize to merge small files.

Data & Analytics

Animation by Mike Mk and lottiefiles: https://lottiefiles.com/user/775169

Failed Tasks in Spark UI - Executers
@adipolak

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Exceptions in Apache Spark Executers logs
@adipolak

Degrading Performance?
You might be suffering
from the
Small Files Syndrome
Adi Polak
Microsoft
@adipolak

About Me
M.Sc & B.Sc - BGU University
ML Researcher @ DT &BGU Cyber
Security lab
Sr. Big Data Engineer @ Akamai
Sr. Software Developer &
Cloud Advocate @ Microsoft
@adipolak
https://www.linkedin.com/in/adi
-polak-68548365/

Agenda
§ The Problem
§ Why it Happens
§ Detect and Mitigate
§ Delta Lake vs Parquet Demo
@adipolak

Query Life Cycle Abstraction
storage storage storage
@adipolak

Query Life Cycle Abstraction
storage
@adipolak

File size matters!
@adipolak
1 Million files of 60 bytes ~ 0.06 GB ~ Reading == 1 M RPCs
1 file of 0.06 GB ~ 60Mb ~ Reading == 1 RPCs

Where can it happen?
• Event streams
• IoT devices, servers, or applications are being translated into KB-scale JSON files during the ingestion
procedure
• Over Paralleled Apache Spark jobs Sub-bullet
• Over Partitioned Hive tables
@adipolak

What to check?
• Data skew - Hive partitions file sizes
• Spark job writers in the Spark History Server UI
• Ingestion file size
@adipolak

Mitigate
• Use file hierarchy - source/api_type/yyyy/mm/dd/hh/mm
• design partitions w/ usage in mind
• Re-partition vs Coalesce
• Databricks Auto Optimize
• SET TBLPROPERTIES (delta.autoOptimize.optimizeWrite = true,
delta.autoOptimize.autoCompact = true)
• Delta Lake Optimize performance
• Compaction (bin-packing)
• ZORDER BY
• delta.targetFileSize
• delta.tuneFileSizesForRewrites
@adipolak

Demo – optimizing read queries
@adipolak

Summary
§ The Problem
§ Why it Happens
§ Detect and Mitigate
§ Delta Lake vs Parquet Demo
@adipolak

“Intellectual growth should commence at birth
and cease only at death.”
― Albert Einstein
@adipolak

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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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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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