The data platform cluster operator service for Hadoop cluster management

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Lakshman Ganesh Rajamani, Staff Site Reliability Engineer, <a href="https://web.archive.org/web/20240305045200/https://twitter.com/lakshmanganesh" class="has-hover-card">@lakshmanganesh</a><br/> Sampath Kumar, Senior Site Reliability Engineer, <a href="https://web.archive.org/web/20240305045200/https://twitter.com/realsampat" class="has-hover-card">@realsampat</a></p> <h2>Introduction</h2> <p>Twitter uses <a href="https://web.archive.org/web/20240305045200/https://hadoop.apache.org/">Apache Hadoop</a> and does not utilize any enterprise Hadoop solutions. The lack of standard cluster management tools was a challenge at the scale we operate on. To address this, we have created our own automation and management tool, the Data Platform Cluster Operator (DCO) service, built in Python, which aims to make SRE operations efficient and provide robust solutions for managing Hadoop clusters across all our data centers and in the cloud.</p> <p>As SREs in the Data platform team, we previously spent a vast majority of our time managing and maintaining Hadoop cluster operations, such as adding or removing hosts to clusters, addressing capacity requirements from users, draining hosts before removal, maintaining the life cycle of the hosts, and ensuring cluster health. The DCO service streamlined these processes and made cluster management a seamless experience.</p> <div class="tweet-error-text">This post is unavailable</div> <div class="bl13-tweet-template"> <div class="embedded-tweet"> <div class="embedded-tweet__loader fill-color--light-blue"> <svg viewbox="-300 -100 1500 1327" xmlns="http://www.w3.org/2000/svg"> <path d="M714.163 519.284L1160.89 0H1055.03L667.137 450.887L357.328 0H0L468.492 681.821L0 1226.37H105.866L515.491 750.218L842.672 1226.37H1200L714.137 519.284H714.163ZM569.165 687.828L521.697 619.934L144.011 79.6944H306.615L611.412 515.685L658.88 583.579L1055.08 1150.3H892.476L569.165 687.854V687.828Z"/> </svg> </div> <div class="embedded-tweet__container"></div> <div class="embedded-tweet__deleted"> <div class="tweet deleted-tweet border-color--neutral-extra-light-gray border-style-dots"> <div class="tweet__container"> <header class="tweet__header"> <div class="tweet__logo"> <svg viewbox="-300 -100 1500 1327" xmlns="http://www.w3.org/2000/svg"> <path d="M714.163 519.284L1160.89 0H1055.03L667.137 450.887L357.328 0H0L468.492 681.821L0 1226.37H105.866L515.491 750.218L842.672 1226.37H1200L714.137 519.284H714.163ZM569.165 687.828L521.697 619.934L144.011 79.6944H306.615L611.412 515.685L658.88 583.579L1055.08 1150.3H892.476L569.165 687.854V687.828Z"/> </svg> </div> </header> <div class="tweet__copy color--neutral-dark-gray type--bold-24"> This post is unavailable. </div> </div> </div> </div> </div> </div> </div> <div class="bl13-rich-text-editor"><h2>High Level Architecture</h2> <div class="tweet-error-text">This post is unavailable</div> <div class="bl13-tweet-template"> <div class="embedded-tweet"> <div class="embedded-tweet__loader fill-color--light-blue"> <svg viewbox="-300 -100 1500 1327" xmlns="http://www.w3.org/2000/svg"> <path d="M714.163 519.284L1160.89 0H1055.03L667.137 450.887L357.328 0H0L468.492 681.821L0 1226.37H105.866L515.491 750.218L842.672 1226.37H1200L714.137 519.284H714.163ZM569.165 687.828L521.697 619.934L144.011 79.6944H306.615L611.412 515.685L658.88 583.579L1055.08 1150.3H892.476L569.165 687.854V687.828Z"/> </svg> </div> <div class="embedded-tweet__container"></div> <div class="embedded-tweet__deleted"> <div class="tweet deleted-tweet border-color--neutral-extra-light-gray border-style-dots"> <div class="tweet__container"> <header class="tweet__header"> <div class="tweet__logo"> <svg viewbox="-300 -100 1500 1327" xmlns="http://www.w3.org/2000/svg"> <path d="M714.163 519.284L1160.89 0H1055.03L667.137 450.887L357.328 0H0L468.492 681.821L0 1226.37H105.866L515.491 750.218L842.672 1226.37H1200L714.137 519.284H714.163ZM569.165 687.828L521.697 619.934L144.011 79.6944H306.615L611.412 515.685L658.88 583.579L1055.08 1150.3H892.476L569.165 687.854V687.828Z"/> </svg> </div> </header> <div class="tweet__copy color--neutral-dark-gray type--bold-24"> This post is unavailable. </div> </div> </div> </div> </div> </div> </div> <div class="bl14-image"> <div> <div class="image image--single b-lazy atc-icon--lazy-load atc-placeholder"> <img class="b-lazy atc-placeholder__spacer atc-placeholder__spacer--height-40" data-src="https://web.archive.org/web/20240305045200/https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter/engineering/en_us/infrastructure/2023/hadoop-cluster-management/hadoop-cluster.png.img.fullhd.medium.png"/> <div class="image__footer border-color--neutral-extra-light-gray "> <span class="image__caption type--roman-14 color--neutral-light-gray"> Figure 1: High level architecture of the DCO service  </span> </div> </div> </div> </div> <div class="bl13-rich-text-editor"><p>The DCO service is implemented using <a href="https://web.archive.org/web/20240305045200/https://flask.palletsprojects.com/en/2.2.x/">Flask</a>, a Python web framework. It is used to expose API endpoints and interact with the database backend. We have a load balancer and web server gateway interface. DCO handles execution orchestration via an internally managed reliable workflow engine for managing hardware servers. One approach that we considered to handle requests is that a request from hadoop administration will be parsed, stored in the database and forwarded to the orchestration engine, which will use a callback feature to update the job state in DCO’s database. In case that the DCO is not available at callback time, there will be a service periodically polling the orchestration engine for plan status. The orchestration engine will implement workflows as plans with sets of tasks that need to be executed on a specific host. Each of these tasks will be retriable for a configured number of times. The orchestration engine will also handle failure scenarios for specific tasks and try different sets of tasks based on failures. A MySQL database will store data and metadata of clusters currently being managed by DCO. The database will receive input from the request management controller which will be responsible for receiving API requests and executing and dispatching tasks. The database is implemented with sharding to allow horizontal scaling if the request volume requires it.</p> <h2>The DCO Tech Stack</h2> <p>The tech stack for the DCO service includes the following components:</p> <ul> <li>Python web server <a href="https://web.archive.org/web/20240305045200/https://flask.palletsprojects.com/en/2.2.x/">Flask</a></li> <li>MySQL database backend</li> <li>Infrastructure for deploying the services</li> <li>Workflow engine for cluster operations use cases.</li> </ul> <h2>Features</h2> <h3>hadoop-admin</h3> <p>hadoop-admin is a python based library which contains the logic to perform hadoop administration operations, look up the status of clusters and the services running on clusters. It also has the ability to execute per-node tasks such as service changes using on-node binaries.</p> <h3>Adding hosts</h3> <p>Adding hosts to a Hadoop Cluster can be a complex task, requiring the installation of various packages, modification of configuration files, and restarting of relevant services. However, with the use of the DCO service, this process can be streamlined into a single step operation. The Site Reliability Engineers can provide a file containing the host names, along with relevant cluster variables, to DCO, which will then proceed to add the hosts to the cluster. The SREs can monitor the progress of this operation through the use of the job ID provided by DCO. This approach enhances efficiency and reduces the likelihood of errors. </p> <h3>Removing hosts</h3> <p>Just like adding hosts, removing hosts is a one step process using the hadoop-admin tool. It not only removes hosts from the cluster but also sends them through the reinstallation process and makes them ready to add to another cluster. </p> <h3><a href="https://web.archive.org/web/20240305045200/https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html">HDFS</a> & <a href="https://web.archive.org/web/20240305045200/https://yarnpkg.com/">YARN</a> quota management</h3> <p>Allocating and deallocating storage space and compute resources can be done via the hadoop-admin tool and DCO stores the history of the changes. This data can be used for charge back for customers. </p> <h3>Keytab Management</h3> <p>All Twitter hadoop clusters use <a href="https://web.archive.org/web/20240305045200/https://web.mit.edu/kerberos/">kerberos</a> authentication. The DCO provisions and manages the lifecycle of the keytabs of each node of every cluster. </p> <h3>Feature Flags</h3> <p>Various feature flags are developed to release the features based on requirement. DCO reads the yaml file of each cluster to check what features that cluster needs. </p> <h3>High Availability</h3> <p>The DCO service runs in each datacenter serving the cluster and cluster operations use cases within that datacenter. DCO does not control or manage the critical data path on hdfs or compute capacity on top of yarn so it does not have a very high uptime SLO. </p> <h2>Conclusion</h2> <p>In conclusion, the new Data Platform Cluster Operator (DCO) service for Hadoop cluster management brings significant advancements in terms of operations, maintenance, and scalability for the SRE team. 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