mirror of https://github.com/apache/druid.git
29 lines
1.9 KiB
Markdown
29 lines
1.9 KiB
Markdown
---
|
||
layout: doc_page
|
||
---
|
||
|
||
Druid vs. Key/Value Stores (HBase/Cassandra/OpenTSDB)
|
||
====================================================
|
||
|
||
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:
|
||
|
||
1. Pre-compute all permutations of possible user queries
|
||
2. Range scans on event data
|
||
|
||
When pre-computing results, the key is the exact parameters of the query, and the value is the result of the query.
|
||
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.
|
||
|
||
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.
|
||
|
||
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.
|