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| <h1 id="druid-vs-kudu">Druid vs Kudu</h1> |
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| <p>Kudu's storage format enables single row updates, whereas updates to existing Druid segments requires recreating the segment, so theoretically<br> |
| the process for updating old values should be higher latency in Druid. However, the requirements in Kudu for maintaining extra head space to store |
| updates as well as organizing data by id instead of time has the potential to introduce some extra latency and accessing |
| of data that is not need to answer a query at query time. </p> |
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| <p>Druid summarizes/rollups up data at ingestion time, which in practice reduces the raw data that needs to be |
| stored significantly (up to 40 times on average), and increases performance of scanning raw data significantly. |
| Druid segments also contain bitmap indexes for fast filtering, which Kudu does not currently support. |
| Druid's segment architecture is heavily geared towards fast aggregates and filters, and for OLAP workflows. Appends are very |
| fast in Druid, whereas updates of older data is higher latency. This is by design as the data Druid is good for is typically event data, |
| and does not need to be updated too frequently. Kudu supports arbitrary primary keys with uniqueness constraints, and |
| efficient lookup by ranges of those keys. Kudu chooses not to include the execution engine, but supports sufficient |
| operations so as to allow node-local processing from the execution engines. This means that Kudu can support multiple frameworks on the same data (eg MR, Spark, and SQL). |
| Druid includes its own query layer that allows it to push down aggregations and computations directly to data nodes for faster query processing.</p> |
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