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.

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

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

Failed Tasks in Spark UI - Executers
@adipolak

Client-Request-ID=------ Retry policy did not allow for a retry: , HTTP status
code=Unknown, Exception=HTTPSConnectionPool(host='-----.net', port=443):
Max retries exceeded with url: /xxxxxxx?restype=container&comp=list
(Caused by
NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at
xxxxxxxx>: Failed to establish a new connection: [Errno 8] nodename nor
servname provided, or not known',)).
HTTPSConnectionPool(ho
st='your_account.blob.cor
e.windows.net', port=443):
Read timed out. (read
timeout=[your timeout])
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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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

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.

Sawtooth Windows for Feature Aggregations

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.

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

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

Re-imagine Data Monitoring with whylogs and Spark

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.

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

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.

Processing Large Datasets for ADAS Applications using Apache Spark

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.

Massive Data Processing in Adobe Using Delta Lake

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!

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Cómo hemos implementado semántica de "Exactly Once" en nuestra base de datos ...

javier ramirez

Los sistemas distribuidos son difíciles. Los sistemas distribuidos de alto rendimiento, más. Latencias de red, mensajes sin confirmación de recibo, reinicios de servidores, fallos de hardware, bugs en el software, releases problemáticas, timeouts... hay un montón de motivos por los que es muy difícil saber si un mensaje que has enviado se ha recibido y procesado correctamente en destino. Así que para asegurar mandas el mensaje otra vez.. y otra... y cruzas los dedos para que el sistema del otro lado tenga tolerancia a los duplicados. QuestDB es una base de datos open source diseñada para alto rendimiento. Nos queríamos asegurar de poder ofrecer garantías de "exactly once", deduplicando mensajes en tiempo de ingestión. En esta charla, te cuento cómo diseñamos e implementamos la palabra clave DEDUP en QuestDB, permitiendo deduplicar y además permitiendo Upserts en datos en tiempo real, añadiendo solo un 8% de tiempo de proceso, incluso en flujos con millones de inserciones por segundo. Además, explicaré nuestra arquitectura de log de escrituras (WAL) paralelo y multithread. Por supuesto, todo esto te lo cuento con demos, para que veas cómo funciona en la práctica.

一比一原版(爱大文凭证书)美国爱荷华大学毕业证如何办理

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[D3T1S02] Aurora Limitless Database Introduction

Amazon Web Services Korea

Amazon Aurora 클러스터를 초당 수백만 건의 쓰기 트랜잭션으로 확장하고 페타바이트 규모의 데이터를 관리할 수 있으며, 사용자 지정 애플리케이션 로직을 생성하거나 여러 데이터베이스를 관리할 필요 없이 Aurora에서 관계형 데이터베이스 워크로드를 단일 Aurora 라이터 인스턴스의 한도 이상으로 확장할 수 있는 Amazon Aurora Limitless Database를 소개합니다.

AIRLINE_SATISFACTION_Data Science Solution on Azure

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

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5. Client-Request-ID=------ Retry policy did not allow for a retry: , HTTP status code=Unknown, Exception=HTTPSConnectionPool(host='-----.net', port=443): Max retries exceeded with url: /xxxxxxx?restype=container&comp=list (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at xxxxxxxx>: Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known',)). HTTPSConnectionPool(ho st='your_account.blob.cor e.windows.net', port=443): Read timed out. (read timeout=[your timeout]) Exceptions in Apache Spark Executers logs @adipolak

6. On-prem Public Cloud

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

8. 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/

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

10. Why it happens? @adipolak

11. Query Life Cycle Abstraction storage storage storage @adipolak

12. Query Life Cycle Abstraction storage @adipolak

13. How Read and Write works? @adipolak

14. 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

15. Detect and Mitigate? @adipolak

16. 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

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

18. 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

19. Demo – optimizing read queries @adipolak

20. @adipolak

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

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

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

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