level of array-based nesting) and many of the interesting compression algorithms mentioned in PowerDrill. Although Druid builds on many of the same principles as other distributed columnar data stores [15],
many of these data stores are designed to be more generic key-value stores [23] and do not sup port computation directly in the storage layer. There are also other
data stores designed for some of the same data warehousing issues
that Druid is meant to solve. These systems include in-memory
databases such as SAP’s HANA [14] and VoltDB [43]. These data
stores lack Druid’slowlatency ingestion characteristics. Druidalso
has native analytical features baked in, similar to ParAccel [34],
however, Druid allows system wide rolling software updates with
Druid is similiar to C-Store [38] and LazyBase [8] in that it has
twosubsystems,aread-optimizedsubsysteminthehistoricalnodes
andawrite-optimizedsubsysteminreal-timenodes. Real-timenodes
are designed to ingest a high volume of append heavy data, and do
not support data updates. Unlike the two aforementioned systems,
Druid is meant for OLAP transactions and not OLTP transactions.
Druid’s low latency data ingestion features share some similar-
ities with Trident/Storm [27] and Spark Streaming [45], however,
both systems are focused on stream processing whereas Druid is
focused on ingestion and aggregation.
Stream processors are great complements to Druid as a means of pre-processing the data before
There are a class of systems that specialize in queries on top of
cluster computing frameworks. Shark [13] is such a system for
queriesontopofSpark,andCloudera’sImpala[9]isanothersystem focused on optimizing query performance on top of HDFS. Druid
historical nodes download data locally and only work with native
Druid indexes. We believe this setup allows for faster query laten Druid leverages a unique combination of algorithms in its archi-
tecture. Although we believe no other data store has the same set
of functionality as Druid, some of Druid’s optimization techniques suchas using inverted indices to perform fast filter sarealsousedin
druid白皮书：http://static.druid.io/docs/druid.pdf
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