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class="row"><div class="col docItemCol_zHA2"><div class="theme-doc-version-banner alert alert--warning margin-bottom--md" role="alert"><div>This is documentation for <!-- -->Apache Hudi<!-- --> <b>0.5.0</b>, which is no longer actively maintained.</div><div class="margin-top--md">For up-to-date documentation, see the <b><a href="/docs/comparison">latest version</a></b> (<!-- -->0.14.1<!-- -->).</div></div><div class="docItemContainer_oiyr"><article><span class="theme-doc-version-badge badge badge--secondary">Version: <!-- -->0.5.0</span><div class="tocCollapsible_aw-L theme-doc-toc-mobile tocMobile_Tx6Y"><button type="button" class="clean-btn tocCollapsibleButton_zr6a">On this page</button></div><div class="theme-doc-markdown markdown"><header><h1>Comparison</h1></header><p>Apache Hudi fills a big void for processing data on top of DFS, and thus mostly co-exists nicely with these technologies. However, |
| it would be useful to understand how Hudi fits into the current big data ecosystem, contrasting it with a few related systems |
| and bring out the different tradeoffs these systems have accepted in their design.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="kudu">Kudu<a class="hash-link" href="#kudu" title="Direct link to heading"></a></h2><p><a href="https://kudu.apache.org" target="_blank" rel="noopener noreferrer">Apache Kudu</a> is a storage system that has similar goals as Hudi, which is to bring real-time analytics on petabytes of data via first |
| class support for <code>upserts</code>. A key differentiator is that Kudu also attempts to serve as a datastore for OLTP workloads, something that Hudi does not aspire to be. |
| Consequently, Kudu does not support incremental pulling (as of early 2017), something Hudi does to enable incremental processing use cases.</p><p>Kudu diverges from a distributed file system abstraction and HDFS altogether, with its own set of storage servers talking to each other via RAFT. |
| Hudi, on the other hand, is designed to work with an underlying Hadoop compatible filesystem (HDFS,S3 or Ceph) and does not have its own fleet of storage servers, |
| instead relying on Apache Spark to do the heavy-lifting. Thu, Hudi can be scaled easily, just like other Spark jobs, while Kudu would require hardware |
| & operational support, typical to datastores like HBase or Vertica. We have not at this point, done any head to head benchmarks against Kudu (given RTTable is WIP). |
| But, if we were to go with results shared by <a href="https://db-blog.web.cern.ch/blog/zbigniew-baranowski/2017-01-performance-comparison-different-file-formats-and-storage-engines" target="_blank" rel="noopener noreferrer">CERN</a> , |
| we expect Hudi to positioned at something that ingests parquet with superior performance.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="hive-transactions">Hive Transactions<a class="hash-link" href="#hive-transactions" title="Direct link to heading"></a></h2><p><a href="https://cwiki.apache.org/confluence/display/Hive/Hive+Transactions" target="_blank" rel="noopener noreferrer">Hive Transactions/ACID</a> is another similar effort, which tries to implement storage like |
| <code>merge-on-read</code>, on top of ORC file format. Understandably, this feature is heavily tied to Hive and other efforts like <a href="https://cwiki.apache.org/confluence/display/Hive/LLAP" target="_blank" rel="noopener noreferrer">LLAP</a>. |
| Hive transactions does not offer the read-optimized storage option or the incremental pulling, that Hudi does. In terms of implementation choices, Hudi leverages |
| the full power of a processing framework like Spark, while Hive transactions feature is implemented underneath by Hive tasks/queries kicked off by user or the Hive metastore. |
| Based on our production experience, embedding Hudi as a library into existing Spark pipelines was much easier and less operationally heavy, compared with the other approach. |
| Hudi is also designed to work with non-hive enginers like Presto/Spark and will incorporate file formats other than parquet over time.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="hbase">HBase<a class="hash-link" href="#hbase" title="Direct link to heading"></a></h2><p>Even though <a href="https://hbase.apache.org" target="_blank" rel="noopener noreferrer">HBase</a> is ultimately a key-value store for OLTP workloads, users often tend to associate HBase with analytics given the proximity to Hadoop. |
| Given HBase is heavily write-optimized, it supports sub-second upserts out-of-box and Hive-on-HBase lets users query that data. However, in terms of actual performance for analytical workloads, |
| hybrid columnar storage formats like Parquet/ORC handily beat HBase, since these workloads are predominantly read-heavy. Hudi bridges this gap between faster data and having |
| analytical storage formats. From an operational perspective, arming users with a library that provides faster data, is more scalable, than managing a big farm of HBase region servers, |
| just for analytics. Finally, HBase does not support incremental processing primitives like <code>commit times</code>, <code>incremental pull</code> as first class citizens like Hudi.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="stream-processing">Stream Processing<a class="hash-link" href="#stream-processing" title="Direct link to heading"></a></h2><p>A popular question, we get is : "How does Hudi relate to stream processing systems?", which we will try to answer here. Simply put, Hudi can integrate with |
| batch (<code>copy-on-write storage</code>) and streaming (<code>merge-on-read storage</code>) jobs of today, to store the computed results in Hadoop. For Spark apps, this can happen via direct |
| integration of Hudi library with Spark/Spark streaming DAGs. In case of Non-Spark processing systems (eg: Flink, Hive), the processing can be done in the respective systems |
| and later sent into a Hudi table via a Kafka topic/DFS intermediate file. In more conceptual level, data processing |
| pipelines just consist of three components : <code>source</code>, <code>processing</code>, <code>sink</code>, with users ultimately running queries against the sink to use the results of the pipeline. |
| Hudi can act as either a source or sink, that stores data on DFS. Applicability of Hudi to a given stream processing pipeline ultimately boils down to suitability |
| of Presto/SparkSQL/Hive for your queries.</p><p>More advanced use cases revolve around the concepts of <a href="https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop" target="_blank" rel="noopener noreferrer">incremental processing</a>, which effectively |
| uses Hudi even inside the <code>processing</code> engine to speed up typical batch pipelines. For e.g: Hudi can be used as a state store inside a processing DAG (similar |
| to how <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.2/ops/state_backends#the-rocksdbstatebackend" target="_blank" rel="noopener noreferrer">rocksDB</a> is used by Flink). This is an item on the roadmap |
| and will eventually happen as a <a href="https://issues.apache.org/jira/browse/HUDI-60" target="_blank" rel="noopener noreferrer">Beam Runner</a></p></div><footer class="theme-doc-footer docusaurus-mt-lg"><div class="theme-doc-footer-edit-meta-row row"><div class="col"><a href="https://github.com/apache/hudi/tree/asf-site/website/versioned_docs/version-0.5.0/comparison.md" target="_blank" rel="noreferrer noopener" class="theme-edit-this-page"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_mt2f"></div></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages navigation"><div class="pagination-nav__item"></div><div class="pagination-nav__item 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