druid相关的时间序列数据库——也用到了倒排相关的优化技术

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

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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