Hadoop Operations - Best Practices from the Field

The document outlines topics covered in "The Impala Cookbook" published by Cloudera. It discusses physical and schema design best practices for Impala, including recommendations for data types, partition design, file formats, and block size. It also covers estimating and managing Impala's memory usage, and how to identify the cause when queries exceed memory limits.

Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features. We will discuss optimizations which provide major performance gains as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.

Presented by: Jonathan Hsieh (Cloudera), Matteo Bertozzi (Cloudera), and Jesse Yates (Salesforce.com)

Did you like it? Check out our E-book: Apache NiFi - A Complete Guide https://ebook.getindata.com/apache-nifi-complete-guide Apache NiFi is one of the most popular services for running ETL pipelines otherwise it’s not the youngest technology. During the talk, there are described all details about migrating pipelines from the old Hadoop platform to the Kubernetes, managing everything as the code, monitoring all corner cases of NiFi and making it a robust solution that is user-friendly even for non-programmers. Author: Albert Lewandowski Linkedin: https://www.linkedin.com/in/albert-lewandowski/ ___ Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets. Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries. https://getindata.com​

Cutting-edge Hadoop clusters are bound to need custom (add-on) services that are not available in the Hadoop distribution of their choice. Agility is crucial for companies to integrate any service into existing large-scale Hadoop clusters with ease. Apache Ambari manages the Hadoop cluster and solves this problem by extending the stack with add-on services, which can be a new Apache project, different Hadoop file system, or internal tool. This talk covers how to create a service definition in Ambari to manage lifecycle commands and configs, plus advanced topics like packaging, installing from multiple repositories, recommending and validating configs using Service Advisor, running custom commands, defining dependencies on configs and other services, and more. We will also cover how to create custom metrics and dashboards using Ambari Metric System and Grafana, generating alerts, and enabling security by authenticating with Kerberos. Further, we will discuss the future of service definitions and how Ambari 3.0 will support custom services through Management Packs to enable Hadoop vendors to release software faster. Speaker Jayush Luniya, Principal Software Engineer, Hortonworks

HBase as the NoSQL database of choice in the Hadoop ecosystem has already been proven itself in scale and in many mission critical workloads in hundreds of companies. Phoenix as the SQL layer on top of HBase, has been increasingly becoming the tool of choice as the perfect complementary for HBase. Phoenix is now being used more and more for super low latency querying and fast analytics across a large number of users in production deployments. In this talk, we will cover what makes Phoenix attractive among current and prospective HBase users, like SQL support, JDBC, data modeling, secondary indexing, UDFs, and also go over recent improvements like Query Server, ODBC drivers, ACID transactions, Spark integration, etc. We will conclude by looking into items in the pipeline and how Phoenix and HBase interacts with other engines like Hive and Spark.

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

The document provides an overview of Apache Spark internals and Resilient Distributed Datasets (RDDs). It discusses: - RDDs are Spark's fundamental data structure - they are immutable distributed collections that allow transformations like map and filter to be applied. - RDDs track their lineage or dependency graph to support fault tolerance. Transformations create new RDDs while actions trigger computation. - Operations on RDDs include narrow transformations like map that don't require data shuffling, and wide transformations like join that do require shuffling. - The RDD abstraction allows Spark's scheduler to optimize execution through techniques like pipelining and cache reuse.

Ozone is an object store for Apache Hadoop that is designed to scale to trillions of objects. It uses a distributed metadata store to avoid single points of failure and enable parallelism. Key components of Ozone include containers, which provide the basic storage and replication functionality, and the Key Space Manager (KSM) which maps Ozone entities like volumes and buckets to containers. The Storage Container Manager manages the container lifecycle and replication.

ORC files were originally introduced in Hive, but have now migrated to an independent Apache project. This has sped up the development of ORC and simplified integrating ORC into other projects, such as Hadoop, Spark, Presto, and Nifi. There are also many new tools that are built on top of ORC, such as Hive’s ACID transactions and LLAP, which provides incredibly fast reads for your hot data. LLAP also provides strong security guarantees that allow each user to only see the rows and columns that they have permission for. This talk will discuss the details of the ORC and Parquet formats and what the relevant tradeoffs are. In particular, it will discuss how to format your data and the options to use to maximize your read performance. In particular, we’ll discuss when and how to use ORC’s schema evolution, bloom filters, and predicate push down. It will also show you how to use the tools to translate ORC files into human-readable formats, such as JSON, and display the rich metadata from the file including the type in the file and min, max, and count for each column.

Data in Hadoop is getting bigger every day, consumers of the data are growing, organizations are now looking at making their Hadoop cluster compliant to federal regulations and commercial demands. Apache Ranger simplifies the management of security policies across all components in Hadoop. Ranger provides granular access controls to data. The deck describes what security tools are available in Hadoop and their purpose then it moves on to discuss in detail Apache Ranger.

The document discusses the Apache Knox Gateway, which is an extensible reverse proxy framework that securely exposes REST APIs and HTTP-based services from Hadoop clusters. It provides features such as support for common Hadoop services, integration with enterprise authentication systems, centralized auditing of REST API access, and service-level authorization controls. The Knox Gateway aims to simplify access to Hadoop services, enhance security by protecting network details and supporting partial SSL, and enable centralized management and control over REST API access.

From: DataWorks Summit 2017 - Munich - 20170406 HBase hast established itself as the backend for many operational and interactive use-cases, powering well-known services that support millions of users and thousands of concurrent requests. In terms of features HBase has come a long way, overing advanced options such as multi-level caching on- and off-heap, pluggable request handling, fast recovery options such as region replicas, table snapshots for data governance, tuneable write-ahead logging and so on. This talk is based on the research for the an upcoming second release of the speakers HBase book, correlated with the practical experience in medium to large HBase projects around the world. You will learn how to plan for HBase, starting with the selection of the matching use-cases, to determining the number of servers needed, leading into performance tuning options. There is no reason to be afraid of using HBase, but knowing its basic premises and technical choices will make using it much more successful. You will also learn about many of the new features of HBase up to version 1.3, and where they are applicable.

This document discusses security features in Apache Kafka including SSL for encryption, SASL/Kerberos for authentication, authorization controls using an authorizer, and securing Zookeeper. It provides details on how these security components work, such as how SSL establishes an encrypted channel and SASL performs authentication. The authorizer implementation stores ACLs in Zookeeper and caches them for performance. Securing Zookeeper involves setting ACLs on Zookeeper nodes and migrating security configurations. Future plans include moving more functionality to the broker side and adding new authorization features.

Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features. We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.

Apache Hive 3 introduces new capabilities for data analytics including materialized views, default columns, constraints, and improved JDBC and Kafka connectors to enable real-time streaming and integration with external systems like Druid; Hive 3 also improves performance and query optimization through a new query result cache, workload management, and cloud storage optimizations. Data Analytics Studio provides self-service analytics on top of Hive 3 through a visual interface to optimize queries, monitor performance, and manage data lifecycles.

The document discusses Apache Tez, a framework for building data processing applications on Hadoop. It provides an introduction to Tez and describes key features like expressing computations as directed acyclic graphs (DAGs), container reuse, dynamic parallelism, integration with YARN timeline service, and recovery from failures. The document also outlines improvements to Tez around performance, debuggability, and status/roadmap.

This document provides an overview and lessons learned from Hadoop. It discusses why Hadoop is used, how MapReduce and HDFS work, tips for integration and operations, and the outlook for the Hadoop community moving forward with real-time capabilities and refined APIs. Key takeaways include only using Hadoop if necessary, fully understanding your data pipeline, and "unboxing the black box" of Hadoop.

This document provides an overview of big data concepts and Hadoop. It discusses the four V's of big data - volume, velocity, variety, and veracity. It then describes how Hadoop uses MapReduce and HDFS to process and store large datasets in a distributed, fault-tolerant and scalable manner across commodity hardware. Key components of Hadoop include the HDFS file system and MapReduce framework for distributed processing of large datasets in parallel.

HDFS Design Principles and the Scale-out-Ability of Distributed Storage. SVForum, Software Architecture & Platform SIG. May 23, 2012.

Hadoop Distributed File System (HDFS) is a distributed file system that stores large datasets across commodity hardware. It is highly fault tolerant, provides high throughput, and is suitable for applications with large datasets. HDFS uses a master/slave architecture where a NameNode manages the file system namespace and DataNodes store data blocks. The NameNode ensures data replication across DataNodes for reliability. HDFS is optimized for batch processing workloads where computations are moved to nodes storing data blocks.

Hadoop Distributed File System (HDFS) evolves from a MapReduce-centric storage system to a generic, cost-effective storage infrastructure where HDFS stores all data of inside the organizations. The new use case presents a new sets of challenges to the original HDFS architecture. One challenge is to scale the storage management of HDFS - the centralized scheme within NameNode becomes a main bottleneck which limits the total number of files stored. Although a typical large HDFS cluster is able to store several hundred petabytes of data, it is inefficient to handle large amounts of small files under the current architecture. In this talk, we introduce our new design and in-progress work that re-architects HDFS to attack this limitation. The storage management is enhanced to a distributed scheme. A new concept of storage container is introduced for storing objects. HDFS blocks are stored and managed as objects in the storage containers instead of being tracked only by NameNode. Storage containers are replicated across DataNodes using a newly-developed high-throughput protocol based on the Raft consensus algorithm. Our current prototype shows that under the new architecture the storage management of HDFS scales 10x better, demonstrating that HDFS is capable of storing billions of files.

Distributed Computing with Apache Hadoop is a technology overview that discusses: 1) Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. 2) Hadoop addresses limitations of traditional distributed computing with an architecture that scales linearly by adding more nodes, moves computation to data instead of moving data, and provides reliability even when hardware failures occur. 3) Core Hadoop components include the Hadoop Distributed File System for storage, and MapReduce for distributed processing of large datasets in parallel on multiple machines.

The NameNode was experiencing high load and instability after being restarted. Graphs showed unknown high load between checkpoints on the NameNode. DataNode logs showed repeated 60000 millisecond timeouts in communication with the NameNode. Thread dumps revealed NameNode server handlers waiting on the same lock, indicating a bottleneck. Source code analysis pointed to repeated block reports from DataNodes to the NameNode as the likely cause of the high load.

HDFS is a distributed file system designed for storing very large data files across commodity servers or clusters. It works on a master-slave architecture with one namenode (master) and multiple datanodes (slaves). The namenode manages the file system metadata and regulates client access, while datanodes store and retrieve block data from their local file systems. Files are divided into large blocks which are replicated across datanodes for fault tolerance. The namenode monitors datanodes and replicates blocks if their replication drops below a threshold.

This document discusses benchmarking Hadoop and big data systems. It provides an overview of common Hadoop benchmarks including microbenchmarks like TestDFSIO, TeraSort, and NNBench which test individual Hadoop components. It also describes BigBench, a benchmark modeled after TPC-DS that aims to test a more complete big data analytics workload using techniques like MapReduce, Hive, and Mahout across structured, semi-structured, and unstructured data. The document emphasizes using Hadoop distributions for administration and both microbenchmarks and full benchmarks like BigBench for evaluation.

This presentation provides a basic overview on Hadoop, Map-Reduce and HDFS related concepts, Configuration and Installation steps and a Sample code.