druid相关的时间序列数据库——也用到了倒排相关的优化技术
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Cattell [6] maintains a great summary about existing Scalable SQL and NoSQL data stores. Hu [18] contributed another great summary for streaming databases. Druid feature-wise sits some-
where between Google’s Dremel [28] and PowerDrill [17]. Druid has most of the features implemented in Dremel (Dremel handles arbitrary nested data structures while Druid only allows for a single
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
no downtime.
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
the data enters Druid.
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
cies.
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
other data stores [26].
druid白皮书：http://static.druid.io/docs/druid.pdf
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