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up-to-date documentation, see the <b><a href="/docs/use_cases">latest version</a></b> (<!-- -->0.14.1<!-- -->).</div></div><div class="docItemContainer_oiyr"><article><span class="theme-doc-version-badge badge badge--secondary">Version: <!-- -->0.11.0</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>Use Cases</h1></header><p>Apache Hudi provides the foundational features required to build a state-of-the-art Lakehouse. |
| The following are examples of use cases for why many choose to use Apache Hudi:</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="a-streaming-data-lake">A Streaming Data Lake<a class="hash-link" href="#a-streaming-data-lake" title="Direct link to heading"></a></h2><p>Apache Hudi is a Streaming Data Lake Platform that unlocks near real-time data ingestion and incremental processing pipelines with ease. |
| This blog post outlines this use case in more depth - <a href="https://hudi.apache.org/blog/2021/07/21/streaming-data-lake-platform" target="_blank" rel="noopener noreferrer">https://hudi.apache.org/blog/2021/07/21/streaming-data-lake-platform</a></p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="near-real-time-ingestion">Near Real-Time Ingestion<a class="hash-link" href="#near-real-time-ingestion" title="Direct link to heading"></a></h3><p>Ingesting data from OLTP sources like (event logs, databases, external sources) into a <a href="http://martinfowler.com/bliki/DataLake.html" target="_blank" rel="noopener noreferrer">Data Lake</a> is a common problem, |
| that is unfortunately solved in a piecemeal fashion, using a medley of ingestion tools. This "raw data" layer of the data lake often forms the bedrock on which |
| more value is created.</p><p>For RDBMS ingestion, Hudi provides <strong>faster loads via Upserts</strong>, as opposed costly & inefficient bulk loads. It's very common to use a change capture solution like |
| <a href="http://debezium.io/" target="_blank" rel="noopener noreferrer">Debezium</a> or <a href="https://docs.confluent.io/platform/current/connect/index" target="_blank" rel="noopener noreferrer">Kafka Connect</a> or |
| <a href="https://sqoop.apache.org/docs/1.4.2/SqoopUserGuide#_incremental_imports" target="_blank" rel="noopener noreferrer">Sqoop Incremental Import</a> and apply them to an |
| equivalent Hudi table on DFS. For NoSQL datastores like <a href="http://cassandra.apache.org/" target="_blank" rel="noopener noreferrer">Cassandra</a> / <a href="http://www.project-voldemort.com/voldemort/" target="_blank" rel="noopener noreferrer">Voldemort</a> / <a href="https://hbase.apache.org/" target="_blank" rel="noopener noreferrer">HBase</a>, |
| even moderately big installations store billions of rows. It goes without saying that <strong>full bulk loads are simply infeasible</strong> and more efficient approaches |
| are needed if ingestion is to keep up with the typically high update volumes.</p><p>Even for immutable data sources like <a href="https://kafka.apache.org" target="_blank" rel="noopener noreferrer">Kafka</a>, there is often a need to de-duplicate the incoming events against what's stored on DFS. |
| Hudi achieves this by <a href="http://hudi.apache.org/blog/2020/11/11/hudi-indexing-mechanisms/" target="_blank" rel="noopener noreferrer">employing indexes</a> of different kinds, quickly and efficiently.</p><p>All of this is seamlessly achieved by the Hudi DeltaStreamer tool, which is maintained in tight integration with rest of the code |
| and we are always trying to add more capture sources, to make this easier for the users. The tool also has a continuous mode, where it |
| can self-manage clustering/compaction asynchronously, without blocking ingestion, significantly improving data freshness.</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="incremental-processing-pipelines">Incremental Processing Pipelines<a class="hash-link" href="#incremental-processing-pipelines" title="Direct link to heading"></a></h3><p>Data Lake ETL typically involves building a chain of tables derived from each other via DAGs expressed as workflows. Workflows often depend on new data being output by |
| multiple upstream workflows and traditionally, availability of new data is indicated by a new DFS Folder/Hive Partition. |
| Let's take a concrete example to illustrate this. An upstream workflow <code>U</code> can create a Hive partition for every hour, with data for that hour (event_time) at the end of each hour (processing_time), providing effective freshness of 1 hour. |
| Then, a downstream workflow <code>D</code>, kicks off immediately after <code>U</code> finishes, and does its own processing for the next hour, increasing the effective latency to 2 hours.</p><p>The above paradigm simply ignores late arriving data i.e when <code>processing_time</code> and <code>event_time</code> drift apart. |
| Unfortunately, in today's post-mobile & pre-IoT world, <strong>late data from intermittently connected mobile devices & sensors are the norm, not an anomaly</strong>. |
| In such cases, the only remedy to guarantee correctness is to reprocess the last few hours worth of data, over and over again each hour, |
| which can significantly hurt the efficiency across the entire ecosystem. For e.g; imagine reprocessing TBs worth of data every hour across hundreds of workflows.</p><p>Hudi comes to the rescue again, by providing a way to consume new data (including late data) from an upstream Hudi table <code>HU</code> at a record granularity (not folders/partitions), |
| apply the processing logic, and efficiently update/reconcile late data with a downstream Hudi table <code>HD</code>. Here, <code>HU</code> and <code>HD</code> can be continuously scheduled at a much more frequent schedule |
| like 15 mins, and providing an end-end latency of 30 mins at <code>HD</code>.</p><p>To achieve this, Hudi has embraced similar concepts from stream processing frameworks like <a href="https://spark.apache.org/docs/latest/streaming-programming-guide#join-operations" target="_blank" rel="noopener noreferrer">Spark Streaming</a> , Pub/Sub systems like <a href="http://kafka.apache.org/documentation/#theconsumer" target="_blank" rel="noopener noreferrer">Kafka</a> |
| <a href="https://flink.apache.org" target="_blank" rel="noopener noreferrer">Flink</a> or database replication technologies like <a href="https://docs.oracle.com/cd/E11882_01/server.112/e16545/xstrm_cncpt.htm#XSTRM187" target="_blank" rel="noopener noreferrer">Oracle XStream</a>. |
| For the more curious, a more detailed explanation of the benefits of Incremental Processing can be found <a href="https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop" target="_blank" rel="noopener noreferrer">here</a></p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="unified-batch-and-streaming">Unified Batch and Streaming<a class="hash-link" href="#unified-batch-and-streaming" title="Direct link to heading"></a></h3><p>The world we live in is polarized - even on data analytics storage - into real-time and offline/batch storage. Typically, real-time <a href="https://en.wikipedia.org/wiki/Data_mart" target="_blank" rel="noopener noreferrer">datamarts</a> |
| are powered by specialized analytical stores such as <a href="http://druid.io/" target="_blank" rel="noopener noreferrer">Druid</a> or <a href="http://www.memsql.com/" target="_blank" rel="noopener noreferrer">Memsql</a> or <a href="https://clickhouse.tech/" target="_blank" rel="noopener noreferrer">Clickhouse</a>, fed by event buses like |
| <a href="https://kafka.apache.org" target="_blank" rel="noopener noreferrer">Kafka</a> or <a href="https://pulsar.apache.org" target="_blank" rel="noopener noreferrer">Pulsar</a>. This model is prohibitively expensive, unless a small fraction of your data lake data |
| needs sub-second query responses such as system monitoring or interactive real-time analysis.</p><p>The same data gets ingested into data lake storage much later (say every few hours or so) and then runs through batch ETL pipelines, with intolerable data freshness |
| to do any kind of near-realtime analytics. On the other hand, the data lakes provide access to interactive SQL engines like Presto/SparkSQL, which can horizontally scale |
| easily and provide return even more complex queries, within few seconds.</p><p>By bringing streaming primitives to data lake storage, Hudi opens up new possibilities by being able to ingest data within few minutes and also author incremental data |
| pipelines that are orders of magnitude faster than traditional batch processing. By bringing <strong>data freshness to a few minutes</strong>, Hudi can provide a much efficient alternative, |
| for a large class of data applications, compared to real-time datamarts. Also, Hudi has no upfront server infrastructure investments |
| and thus enables faster analytics on much fresher analytics, without increasing the operational overhead. This external <a href="https://www.analyticsinsight.net/can-big-data-solutions-be-affordable/" target="_blank" rel="noopener noreferrer">article</a> |
| further validates this newer model.</p><h2 class="anchor anchorWithStickyNavbar_y2LR" id="cloud-native-tables">Cloud-Native Tables<a class="hash-link" href="#cloud-native-tables" title="Direct link to heading"></a></h2><p>Apache Hudi makes it easy to define tables, manage schema, metadata, and bring SQL semantics to cloud file storage. |
| Some may first hear about Hudi as an "open table format". While this is true, it is just one layer the full Hudi stack. |
| The term “table format” is new and still means many things to many people. Drawing an analogy to file formats, a table |
| format simply consists of : the file layout of the table, table’s schema and metadata tracking changes to the table. |
| Hudi is not a table format alone, but it does implement one internally. </p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="schema-management">Schema Management<a class="hash-link" href="#schema-management" title="Direct link to heading"></a></h3><p>A key component of a table is the schema of that table. Apache Hudi provides flexibility to enforce schemas, but also allow |
| schema evolution to ensure pipeline resilience to changes. Hudi uses Avro schemas to store, manage and evolve a table’s |
| schema. Currently, Hudi enforces schema-on-write, which although stricter than schema-on-read, is adopted widely in the |
| stream processing world to ensure pipelines don't break from non backwards compatible changes.</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="acid-transactions">ACID Transactions<a class="hash-link" href="#acid-transactions" title="Direct link to heading"></a></h3><p>Along with a table, Apache Hudi brings ACID transactional guarantees to a data lake. |
| Hudi ensures atomic writes, by way of publishing commits atomically to a <a href="/docs/timeline">timeline</a>, stamped with an |
| instant time that denotes the time at which the action |
| is deemed to have occurred. Unlike general purpose file version control, Hudi draws clear distinction between writer processes |
| (that issue user’s upserts/deletes), table services (that write data/metadata to optimize/perform bookkeeping) and readers |
| (that execute queries and read data). Hudi provides snapshot isolation between all three types of processes, meaning they |
| all operate on a consistent snapshot of the table. Hudi provides <a href="https://cwiki.apache.org/confluence/display/HUDI/RFC+-+22+%3A+Snapshot+Isolation+using+Optimistic+Concurrency+Control+for+multi-writers" target="_blank" rel="noopener noreferrer">optimistic concurrency control</a> |
| (OCC) between writers, while providing lock-free, non-blocking MVCC based concurrency control between writers and |
| table-services and between different table services.</p><p>Projects that solely rely on OCC deal with competing operations, by either implementing a lock or relying on atomic renames. |
| Such approaches are optimistic that real contention never happens and resort to failing one of the writer operations if |
| conflicts occur, which can cause significant resource wastage or operational overhead. Imagine a scenario of two writer |
| processes : an ingest writer job producing new data every 30 minutes and a deletion writer job that is enforcing GDPR |
| taking 2 hours to issue deletes. If there were to overlap on the same files (very likely to happen in real situations |
| with random deletes), the deletion job is almost guaranteed to starve and fail to commit each time, wasting tons of |
| cluster resources. Hudi takes a very different approach that we believe is more apt for lake transactions, which are |
| typically long-running. For e.g async compaction that can keep deleting records in the background without blocking the ingest job. |
| This is implemented via a file level, log based concurrency control protocol which orders actions based on their start instant times on the timeline.</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="efficient-upserts-and-deletes">Efficient Upserts and Deletes<a class="hash-link" href="#efficient-upserts-and-deletes" title="Direct link to heading"></a></h3><p>While ACID transactions opens the door for Upserts and Deletes, Hudi also unlocks special capabilities like clustering, |
| indexing, and z-ordering which allows users to optimize for efficiency in Deletions and Upserts. Specifically, users can |
| cluster older event log data based on user_id, such that, queries that evaluate candidates for data deletion can do so, while |
| more recent partitions are optimized for query performance and clustered on say timestamp. </p><p>Hudi also offers efficient ways of dealing with large write amplification, resulting from random deletes based on user_id |
| (or any secondary key), by way of the <code>Merge On Read</code> table types. Hudi's elegant log based concurrency control, ensures |
| that the ingestion/writing can continue happening, as a background compaction job amortizes the cost of rewriting data to enforce deletes.</p><h3 class="anchor anchorWithStickyNavbar_y2LR" id="time-travel">Time-Travel<a class="hash-link" href="#time-travel" title="Direct link to heading"></a></h3><p>Apache Hudi unlocks the ability to write time travel queries, which means you can query the previous state of the data. |
| This is particularly useful for a few use cases. </p><ul><li>Rollbacks - Easily revert back to a previous version of the table.</li><li>Debugging - Inspect previous versions of data to understand how it has changed over time.</li><li>Audit History - Have a trail of commits that helps you see how, who, and when altered the data over time.</li></ul><h2 class="anchor anchorWithStickyNavbar_y2LR" id="data-lake-performance-optimizations">Data Lake Performance Optimizations<a class="hash-link" href="#data-lake-performance-optimizations" title="Direct link to heading"></a></h2><p>Apache Hudi offers several cutting edge services which help you achieve industry leading performance and significant |
| cost savings for your data lake.</p><p>Some examples of the Apache Hudi services that make this performance optimization easy include: </p><ul><li><a href="/docs/file_sizing">Auto File Sizing</a> - to solve the "small files" problem.</li><li><a href="/docs/clustering">Clustering</a> - to co-locate data next to each other.</li><li><a href="/docs/compaction">Compaction</a> - to allow tuning of low latency ingestion and fast read queries. </li><li><a href="/docs/indexing">Indexing</a> - for efficient upserts and deletes.</li><li>Multi-Dimensional Partitioning (Z-Ordering) - Traditional folder style partitioning on low-cardinality, while also |
| Z-Ordering data within files based on high-cardinality</li><li>Metadata Table - No more slow S3 file listings or throttling.</li><li><a href="/docs/hoodie_cleaner">Auto Cleaning</a> - Keeps your storage costs in check by automatically removing old versions of files.</li></ul></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-0.11.0/use_cases.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" 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