Apache
/
druid
mirror of https://github.com/apache/druid.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Update druid-vs-kudu.md (#11470) 

small typo - "need" to "needed"
This commit is contained in:
benkrug 2021-07-21 07:58:14 -07:00 committed by GitHub
parent 6ce3b6ca2d
commit 167c45260c
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
docs/comparisons/druid-vs-kudu.md
View File

@ -26,13 +26,13 @@ title: "Apache Druid vs Kudu"
Kudu's storage format enables single row updates, whereas updates to existing Druid segments requires recreating the segment, so theoretically
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.
of data that is not needed to answer a query at query time.
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,
fast in Druid, whereas updates of older data are 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 (e.g., MR, Spark, and SQL).