Apache HBase Performance Tuning

The tech talk was gieven by Ranjeeth Kathiresan, Salesforce Senior Software Engineer & Gurpreet Multani, Salesforce Principal Software Engineer in June 2017.

The document discusses Facebook's use of HBase to store messaging data. It provides an overview of HBase, including its data model, performance characteristics, and how it was a good fit for Facebook's needs due to its ability to handle large volumes of data, high write throughput, and efficient random access. It also describes some enhancements Facebook made to HBase to improve availability, stability, and performance. Finally, it briefly mentions Facebook's migration of messaging data from MySQL to their HBase implementation.

This document discusses file system usage in HBase. It provides an overview of the three main file types in HBase: write-ahead logs (WALs), data files, and reference files. It describes durability semantics, IO fencing techniques for region server recovery, and how HBase leverages data locality through short circuit reads, checksums, and block placement hints. The document is intended help understand HBase's interactions with HDFS for tuning IO performance.

Apache HBase is the Hadoop opensource, distributed, versioned storage manager well suited for random, realtime read/write access. This talk will give an overview on how HBase achieve random I/O, focusing on the storage layer internals. Starting from how the client interact with Region Servers and Master to go into WAL, MemStore, Compactions and on-disk format details. Looking at how the storage is used by features like snapshots, and how it can be improved to gain flexibility, performance and space efficiency.

Anoop Sam John and Ramkrishna Vasudevan (Intel) HBase provides an LRU based on heap cache but its size (and so the total data size that can be cached) is limited by Java’s max heap space. This talk highlights our work under HBASE-11425 to allow the HBase read path to work directly from the off-heap area.

This document provides best practices for YARN administrators and application developers. For administrators, it discusses YARN configuration, enabling ResourceManager high availability, configuring schedulers like Capacity Scheduler and Fair Scheduler, sizing containers, configuring NodeManagers, log aggregation, and metrics. For application developers, it discusses whether to use an existing framework or develop a native application, understanding YARN components, writing the client, and writing the ApplicationMaster.

Todd Lipcon presents a solution to avoid full garbage collections (GCs) in HBase by using MemStore-Local Allocation Buffers (MSLABs). The document outlines that write operations in HBase can cause fragmentation in the old generation heap, leading to long GC pauses. MSLABs address this by allocating each MemStore's data into contiguous 2MB chunks, eliminating fragmentation. When MemStores flush, the freed chunks are large and contiguous. With MSLABs enabled, the author saw basically zero full GCs during load testing. MSLABs improve performance and stability by preventing GC pauses caused by fragmentation.

Apache Hive is a data warehousing system for large volumes of data stored in Hadoop. However, the data is useless unless you can use it to add value to your company. Hive provides a SQL-based query language that dramatically simplifies the process of querying your large data sets. That is especially important while your data scientists are developing and refining their queries to improve their understanding of the data. In many companies, such as Facebook, Hive accounts for a large percentage of the total MapReduce queries that are run on the system. Although Hive makes writing large data queries easier for the user, there are many performance traps for the unwary. Many of them are artifacts of the way Hive has evolved over the years and the requirement that the default behavior must be safe for all users. This talk will present examples of how Hive users have made mistakes that made their queries run much much longer than necessary. It will also present guidelines for how to get better performance for your queries and how to look at the query plan to understand what Hive is doing.

Flink Forward San Francisco 2022. With a real-time processing engine like Flink and a transactional storage layer like Hudi, it has never been easier to build end-to-end low-latency data platforms connecting sources like Kafka to data lake storage. Come learn how to blend Lakehouse architectural patterns with real-time processing pipelines with Flink and Hudi. We will dive deep on how Flink can leverage the newest features of Hudi like multi-modal indexing that dramatically improves query and write performance, data skipping that reduces the query latency by 10x for large datasets, and many more innovations unique to Flink and Hudi. by Ethan Guo & Kyle Weller

This document introduces HBase, an open-source, non-relational, distributed database modeled after Google's BigTable. It describes what HBase is, how it can be used, and when it is applicable. Key points include that HBase stores data in columns and rows accessed by row keys, integrates with Hadoop for MapReduce jobs, and is well-suited for large datasets, fast random access, and write-heavy applications. Common use cases involve log analytics, real-time analytics, and messages-centered systems.

Building highly efficient data lakes using Apache Hudi (Incubating) Even with the exponential growth in data volumes, ingesting/storing/managing big data remains unstandardized & in-efficient. Data lakes are a common architectural pattern to organize big data and democratize access to the organization. In this talk, we will discuss different aspects of building honest data lake architectures, pin pointing technical challenges and areas of inefficiency. We will then re-architect the data lake using Apache Hudi (Incubating), which provides streaming primitives right on top of big data. We will show how upserts & incremental change streams provided by Hudi help optimize data ingestion and ETL processing. Further, Apache Hudi manages growth, sizes files of the resulting data lake using purely open-source file formats, also providing for optimized query performance & file system listing. We will also provide hands-on tools and guides for trying this out on your own data lake. Speaker: Vinoth Chandar (Uber) Vinoth is Technical Lead at Uber Data Infrastructure Team

This talk delves into the many ways that a user has to use HBase in a project. Lars will look at many practical examples based on real applications in production, for example, on Facebook and eBay and the right approach for those wanting to find their own implementation. He will also discuss advanced concepts, such as counters, coprocessors and schema design.

Apache Tez is a framework for accelerating Hadoop query processing. It is based on expressing a computation as a dataflow graph and executing it in a highly customizable way. Tez is built on top of YARN and provides benefits like better performance, predictability, and utilization of cluster resources compared to traditional MapReduce. It allows applications to focus on business logic rather than Hadoop internals.

Learn about Hudi's architecture, concurrency control mechanisms, table services and tools. By : Abhishek Modi, Balajee Nagasubramaniam, Prashant Wason, Satish Kotha, Nishith Agarwal

This document provides an overview of HBase and why NoSQL databases like HBase were developed. It discusses how relational databases do not scale horizontally well with large amounts of data. HBase was created by Google to address these scaling issues and was inspired by their BigTable database. The document explains the HBase data model with rows, columns, and versions. It describes how data is stored physically in HFiles and served from memory and disks. Basic operations like put, get, and scan are also covered.

Apache Kafka is a distributed publish-subscribe messaging system that allows both publishing and subscribing to streams of records. It uses a distributed commit log that provides low latency and high throughput for handling real-time data feeds. Key features include persistence, replication, partitioning, and clustering.

This document discusses supporting Apache HBase and improving troubleshooting and supportability. It introduces two Cloudera employees who work on HBase support and provides an overview of typical troubleshooting scenarios for HBase like performance degradation, process crashes, and inconsistencies. The agenda covers using existing tools like logs and metrics to troubleshoot HBase performance issues with a general approach, and introduces htop as a real-time monitoring tool for HBase.

Speakers: Liang Xie and Honghua Feng (Xiamoi) This talk covers the HBase environment at Xiaomi, including thoughts and practices around latency, hardware/OS/VM configuration, GC tuning, the use of a new write thread model and reverse scan, and block index optimization. It will also include some discussion of planned JIRAs based on these approaches.

Netezza provides workload management options to efficiently service user queries. It allows restricting the maximum concurrent jobs, creating resource sharing groups to control resource allocation disproportionately, and uses multiple schedulers like gatekeeper and GRA. Gatekeeper queues jobs and schedules based on priority and resource availability. GRA allocates resources to jobs based on user's resource group. Short queries can be prioritized using short query bias which reserves system resources for such queries.

The document discusses using Netezza query plans to improve performance. It explains that Netezza generates a query plan detailing the optimal execution path determined by the query optimizer. The query plan shows snippets that read/join data and where they execute. Key points in plans like estimated rows, costs and distributions are examined to identify issues. Common performance problems and steps for analysis are outlined, like generating statistics, changing distributions and rewriting queries.

Parallel processing involves executing multiple tasks simultaneously using multiple cores or processors. It can provide performance benefits over serial processing by reducing execution time. When developing parallel applications, developers must identify independent tasks that can be executed concurrently and avoid issues like race conditions and deadlocks. Effective parallelization requires analyzing serial code to find optimization opportunities, designing and implementing concurrent tasks, and testing and tuning to maximize performance gains.

There are two main types of relational database management systems (RDBMS): row-based and columnar. Row-based systems store all of a row's data contiguously on disk, while columnar systems store each column's data together across all rows. Columnar databases are generally better for read-heavy workloads like data warehousing that involve aggregating or retrieving subsets of columns, whereas row-based databases are better for transactional systems that require updating or retrieving full rows frequently. The optimal choice depends on the specific access patterns and usage of the data.

The document discusses HDFS architecture and components. It describes how HDFS uses NameNodes and DataNodes to store and retrieve file data in a distributed manner across clusters. The NameNode manages the file system namespace and regulates access to files by clients. DataNodes store file data in blocks and replicate them for fault tolerance. The document outlines the write and read workflows in HDFS and how NameNodes and DataNodes work together to manage data storage and access.

The document provides an overview of the fundamentals of Websphere MQ including: - The key MQ objects like messages, queues, channels and how they work - Basic MQ administration tasks like defining, displaying, altering and deleting MQ objects using MQSC commands - Hands-on exercises are included to demonstrate programming with MQ and administering MQ objects

The document discusses project risk management. It defines project risk as the loss multiplied by the likelihood. Successful project leaders plan thoroughly to understand challenges, anticipate problems, and minimize variation. Project failures can occur if objectives are impossible, deliverables are possible but other objectives are unrealistic, or feasible deliverables and objectives but insufficient planning. Risk management includes qualitative and quantitative risk assessment to understand probability and impact of risks. It is important to document risks, have risk management plans, and regularly review assumptions and risks.

This document provides an introduction to the Netezza database appliance, including its architecture and key components. The Netezza uses an Asymmetric Massively Parallel Processing (AMPP) architecture with an array of servers (S-Blades) connected to disks. Each S-Blade contains a Database Accelerator card that offloads processing from the CPU. The document outlines the various hardware components and how they work together to process queries in parallel. It also defines common Netezza objects like users, groups, tables and databases that can be created and managed.

This document discusses considerations for data modeling on Netezza appliances to optimize performance. It recommends distributing data uniformly across snippet processors to maximize parallel processing. When joining tables, the distribution key should match join columns to keep processors independent. Zone maps and clustered tables can reduce data reads from disk. Materialized views on frequently accessed columns further improve performance for single table and join queries.

We start by looking at distributed database features that impact latency. Then we take a deeper look at the HBase read and write paths with a focus on request latency. We examine the sources of latency and how to minimize them.

Introduction to HBase. HBase is a NoSQL databases which experienced a tremendous increase in popularity during the last years. Large companies like Facebook, LinkedIn, Foursquare are using HBase. In this presentation we will address questions like: what is HBase?, and compared to relational databases?, what is the architecture?, how does HBase work?, what about the schema design?, what about the IT ressources?. Questions that should help you consider whether this solution might be suitable in your case.

This document provides an overview of HBase, including: - HBase is a distributed, scalable, big data store modeled after Google's BigTable. It provides a fault-tolerant way to store large amounts of sparse data. - HBase is used by large companies to handle scaling and sparse data better than relational databases. It features automatic partitioning, linear scalability, commodity hardware, and fault tolerance. - The document discusses HBase operations, schema design best practices, hardware recommendations, alerting, backups and more. It provides guidance on designing keys, column families and cluster configuration to optimize performance for read and write workloads.

Elastic HBase on Mesos aims to improve resource utilization of HBase clusters by running HBase in Docker containers managed by Mesos and Marathon. This allows HBase clusters to dynamically scale based on varying workload demands, increases utilization by running mixed workloads on shared resources, and simplifies operations through standard containerization. Key benefits include easier management, higher efficiency through elastic scaling and resource sharing, and improved cluster tunability.

This document summarizes a presentation about optimizing for low latency in HBase. It discusses how to measure latency, the write and read paths in HBase, sources of latency like garbage collection and compactions, and techniques for reducing latency like streaming puts, block caching, and timeline consistency. The key points are that single puts can achieve millisecond latency while garbage collection and machine failures can cause pauses of 10s of milliseconds to seconds, and optimizing for the "magical 1%" of requests after the 99th percentile is important to improve average latency.

This document discusses tuning Linux and PostgreSQL for performance. It recommends: - Tuning Linux kernel parameters like huge pages, swappiness, and overcommit memory. Huge pages can improve TLB performance. - Tuning PostgreSQL parameters like shared_buffers, work_mem, and checkpoint_timeout. Shared_buffers stores the most frequently accessed data. - Other tips include choosing proper hardware, OS, and database based on workload. Tuning queries and applications can also boost performance.

Hadoop is an open-source framework that processes large datasets in a distributed manner across commodity hardware. It uses a distributed file system (HDFS) and MapReduce programming model to store and process data. Hadoop is highly scalable, fault-tolerant, and reliable. It can handle data volumes and variety including structured, semi-structured and unstructured data.

Adobe has packaged HBase in Docker containers and uses Marathon and Mesos to schedule them—allowing us to decouple the RegionServer from the host, express resource requirements declaratively, and open the door for unassisted real-time deployments, elastic (up and down) real-time scalability, and more. In this talk, you'll hear what we've learned and explain why this approach could fundamentally change HBase operations.

The document discusses how HDFS architecture has evolved to meet new requirements for higher scalability, availability, and improved random read performance. It summarizes the key aspects of HDFS architecture in 2010, including limitations, and improvements made since then, such as read pipeline optimizations, federated namespaces, and high availability name nodes. It also outlines future directions for HDFS architecture.

This document provides an overview and best practices for operating HBase clusters. It discusses HBase and Hadoop architecture, how to set up an HBase cluster including Zookeeper and region servers, high availability considerations, scaling the cluster, backup and restore processes, and operational best practices around hardware, disks, OS, automation, load balancing, upgrades, monitoring and alerting. It also includes a case study of a 110 node HBase cluster.

A deeper look at the HBase read and write paths with a focus on request latency. We look at sources of latency and how to minimize them.

This document provides an overview of HBase architecture and advanced usage topics. It discusses course credit requirements, HBase architecture components like storage, write path, read path, files, region splits and more. It also covers advanced topics like secondary indexes, search integration, transactions and bloom filters. The document emphasizes that HBase uses log-structured merge trees for efficient data handling and operates at the disk transfer level rather than disk seek level for performance. It also provides details on various classes involved in write-ahead logging.