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new 167c452 Update druid-vs-kudu.md (#11470)
167c452 is described below
commit 167c45260c76057b9856bd073661365663bd80f2
Author: benkrug <[email protected]>
AuthorDate: Wed Jul 21 07:58:14 2021 -0700
Update druid-vs-kudu.md (#11470)
small typo - "need" to "needed"
---
docs/comparisons/druid-vs-kudu.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/docs/comparisons/druid-vs-kudu.md
b/docs/comparisons/druid-vs-kudu.md
index aca743d..b992a16 100644
--- a/docs/comparisons/druid-vs-kudu.md
+++ b/docs/comparisons/druid-vs-kudu.md
@@ -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).
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