2018-11-13 12:38:37 -05:00
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2015-11-06 15:21:51 -05:00
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---
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layout: doc_page
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2018-12-12 23:42:12 -05:00
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title: "Druid vs. Key/Value Stores (HBase/Cassandra/OpenTSDB)"
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2015-11-06 15:21:51 -05:00
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---
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2018-12-12 23:42:12 -05:00
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# Druid vs. Key/Value Stores (HBase/Cassandra/OpenTSDB)
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2015-11-06 15:21:51 -05:00
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Druid is highly optimized for scans and aggregations, it supports arbitrarily deep drill downs into data sets. This same functionality
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is supported in key/value stores in 2 ways:
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1. Pre-compute all permutations of possible user queries
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2. Range scans on event data
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When pre-computing results, the key is the exact parameters of the query, and the value is the result of the query.
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The queries return extremely quickly, but at the cost of flexibility, as ad-hoc exploratory queries are not possible with
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pre-computing every possible query permutation. Pre-computing all permutations of all ad-hoc queries leads to result sets
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that grow exponentially with the number of columns of a data set, and pre-computing queries for complex real-world data sets
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can require hours of pre-processing time.
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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.
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Aggregations are done by issuing range scans on this data. Timeseries specific databases such as OpenTSDB use this approach.
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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,
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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.
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When the number of rows to scan gets large, this limitation can greatly reduce performance. It is also harder to achieve good
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locality with key/value stores because most don’t support pushing down aggregates to the storage layer.
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For arbitrary exploration of data (flexible data filtering), Druid's custom column format enables ad-hoc queries without pre-computation. The format
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also enables fast scans on columns, which is important for good aggregation performance.
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