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Hovering over a job provides detailed information including the full set of counters. Inviso correlates the various stages and lays them out in a swimlane diagram to show the parallelism. Stages of a Hive or Pig script might execute in serially or parallel impacting the total runtime. Simply finding a job and the corresponding hadoop resources doesn’t make it any easier to understand the performance. Others use it to identify who is using a specific table in case they want to change the structure or retire the table.
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For example, when we last upgraded Hive, the new version had keyword conflicts with some existing scripts and we were able to identify the scripts and owners to upgrade prior to rolling out the new version of Hive. Internally, we use the index to search for dependencies and scripts when modifying/deprecating/upgrading datasources, UDFs, etc. Since the index contains the full text of hive or pig script, searching for table or UDF usage is possible as well. In addition to the interface provided by Inviso, the ElasticSearch index is quite effective for other use cases. Being able to look back over months of different runs of the same job allows for detailed analysis of how the job evolves over time. Clicking the links will take you directly to the specific page for that job. The search results are displayed in a concise table reverse ordered by time with continuous scrollback and links to various tools like the job history page, Genie, or Lipstick. With the ability to use the full lucene query syntax, finding jobs is straightforward and powerful.
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Indexing job configurations into ElasticSearch is trivial because the structure is simple and flat. To simplify this process, Inviso indexes every job configuration across all clusters into ElasticSearch and provides a simple search interface to query. Searching for Jobsįinding a specific job run should be easy, but with each Hive or Pig script abstracting multiple Hadoop jobs, finding and pulling together the full execution workflow can be painful. Inviso provides an easy interface to find jobs across all clusters, access other related tools, visualize performance, make detailed information accessible, and understand the environment in which jobs run.
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Netflix is pleased to add Inviso to our open source portfolio under the Apache License v2.0 and is available on github. Inviso is a job search and visualization tool intended to help big data users understand execution performance. To help answer these questions and empower our platform users to explore and improve their job performance, we created a tool: Inviso ( latin: to go to see, visit, inspect, look at). By the time someone notices a problem, the cluster that ran the query, along with detailed information, may already be gone or archived. These questions can be hard to answer in our environment because clusters are not persistent.
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Why did my job run slower today than yesterday?Ĭan we expand the cluster to speed up my job? Some of the most common questions we hear are:
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Navigating the maze of tools, logs, and data to gather information about a specific run can be difficult and time consuming.
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We have hundreds of platform users ranging from running casual queries to ETL developers and data scientists running tens to hundreds of queries every day. However, as a user of the system, understanding where and how a particular job executes can be confusing. This cohesive infrastructure abstracts all of the orchestration from the execution and allows the platform team to be flexible and adapt to dynamic environments without impacting users of the system. Genie, our execution service, abstracts the configuration and resource management for job submissions by providing a centralized service to query across all big data resources.
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We experiment with new software and perform live upgrades by simply diverting jobs from one cluster to another or adjust the size and number of clusters based on need as opposed to capacity. Decentralizing the data warehouse frees us to explore new ways to manage big data infrastructure but also introduces a new set of challenges.įrom a platform management perspective, being able to run multiple clusters isolated by concerns is both convenient and effective. This differentiates us from the more traditional configuration where Hadoop’s distributed file system is the storage medium with data and compute residing in the same cluster. One of the key points from the article is that Netflix leverages Amazon’s Simple Storage Service (S3) as the “source of truth” for all data warehousing. In a post last year we discussed our big data architecture and the advantages of working with big data in the cloud (read more here).