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| <h1 id="apache-druid-incubating-vs-key-value-stores-hbase-cassandra-opentsdb">Apache Druid (incubating) vs. Key/Value Stores (HBase/Cassandra/OpenTSDB)</h1> |
| |
| <p>Druid is highly optimized for scans and aggregations, it supports arbitrarily deep drill downs into data sets. This same functionality |
| is supported in key/value stores in 2 ways:</p> |
| |
| <ol> |
| <li>Pre-compute all permutations of possible user queries</li> |
| <li>Range scans on event data</li> |
| </ol> |
| |
| <p>When pre-computing results, the key is the exact parameters of the query, and the value is the result of the query.<br> |
| The queries return extremely quickly, but at the cost of flexibility, as ad-hoc exploratory queries are not possible with |
| pre-computing every possible query permutation. Pre-computing all permutations of all ad-hoc queries leads to result sets |
| that grow exponentially with the number of columns of a data set, and pre-computing queries for complex real-world data sets |
| can require hours of pre-processing time.</p> |
| |
| <p>The other approach to using key/value stores for aggregations to use the dimensions of an event as the key and the event measures as the value. |
| Aggregations are done by issuing range scans on this data. Timeseries specific databases such as OpenTSDB use this approach. |
| One of the limitations here is that the key/value storage model does not have indexes for any kind of filtering other than prefix ranges, |
| which can be used to filter a query down to a metric and time range, but cannot resolve complex predicates to narrow the exact data to scan. |
| When the number of rows to scan gets large, this limitation can greatly reduce performance. It is also harder to achieve good |
| locality with key/value stores because most don’t support pushing down aggregates to the storage layer.</p> |
| |
| <p>For arbitrary exploration of data (flexible data filtering), Druid's custom column format enables ad-hoc queries without pre-computation. The format |
| also enables fast scans on columns, which is important for good aggregation performance.</p> |
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