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| <h1 id="apache-druid-incubating-vs-redshift">Apache Druid (incubating) vs Redshift</h1> |
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| <h3 id="how-does-druid-compare-to-redshift">How does Druid compare to Redshift?</h3> |
| |
| <p>In terms of drawing a differentiation, Redshift started out as ParAccel (Actian), which Amazon is licensing and has since heavily modified.</p> |
| |
| <p>Aside from potential performance differences, there are some functional differences:</p> |
| |
| <h3 id="real-time-data-ingestion">Real-time data ingestion</h3> |
| |
| <p>Because Druid is optimized to provide insight against massive quantities of streaming data; it is able to load and aggregate data in real-time.</p> |
| |
| <p>Generally traditional data warehouses including column stores work only with batch ingestion and are not optimal for streaming data in regularly.</p> |
| |
| <h3 id="druid-is-a-read-oriented-analytical-data-store">Druid is a read oriented analytical data store</h3> |
| |
| <p>Druid’s write semantics are not as fluid and does not support full joins (we support large table to small table joins). Redshift provides full SQL support including joins and insert/update statements.</p> |
| |
| <h3 id="data-distribution-model">Data distribution model</h3> |
| |
| <p>Druid’s data distribution is segment-based and leverages a highly available "deep" storage such as S3 or HDFS. Scaling up (or down) does not require massive copy actions or downtime; in fact, losing any number of Historical processes does not result in data loss because new Historical processes can always be brought up by reading data from "deep" storage.</p> |
| |
| <p>To contrast, ParAccel’s data distribution model is hash-based. Expanding the cluster requires re-hashing the data across the nodes, making it difficult to perform without taking downtime. Amazon’s Redshift works around this issue with a multi-step process:</p> |
| |
| <ul> |
| <li>set cluster into read-only mode</li> |
| <li>copy data from cluster to new cluster that exists in parallel</li> |
| <li>redirect traffic to new cluster</li> |
| </ul> |
| |
| <h3 id="replication-strategy">Replication strategy</h3> |
| |
| <p>Druid employs segment-level data distribution meaning that more processes can be added and rebalanced without having to perform a staged swap. The replication strategy also makes all replicas available for querying. Replication is done automatically and without any impact to performance.</p> |
| |
| <p>ParAccel’s hash-based distribution generally means that replication is conducted via hot spares. This puts a numerical limit on the number of nodes you can lose without losing data, and this replication strategy often does not allow the hot spare to help share query load.</p> |
| |
| <h3 id="indexing-strategy">Indexing strategy</h3> |
| |
| <p>Along with column oriented structures, Druid uses indexing structures to speed up query execution when a filter is provided. Indexing structures do increase storage overhead (and make it more difficult to allow for mutation), but they also significantly speed up queries.</p> |
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| <p>ParAccel does not appear to employ indexing strategies.</p> |
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