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In this example it doesn’t seem like a dramatic difference, but imagine that the number of nodes is 100, while the partitioning factor could still be e. g. 10 in Druid or Pinot.To mitigate this problem, the largest ClickHouse cluster at Yandex (of hundreds of nodes) is in fact split into many “subclusters” of a few dozens of nodes each.
ClickHouse more resembles “traditional” databases like PostgreSQL. Also developers of the both systems probably wouldn’t want to do that anyway, because the approach of both has it’s pros and cons, it’s not that one is totally better than another.The “master” node in Druid (and neither in Pinot) is not responsible for persistence of the metadata of the data segments in the cluster, and the current mapping between segments and query processing nodes, on which the segments are loaded. Here's a link to Druid's open source repository on GitHub.
support for XML data structures, and/or support for XPath, XQuery or XSLT.The concept of 'projections' can be used instead of indexes.nearly full SQL99 without support for indexes and triggersMethods for storing different data on different nodesSegmentation, Projection Segmentation, Hash SegmentationMethods for redundantly storing data on multiple nodesMethods to ensure consistency in a distributed systemSupport to ensure data integrity after non-atomic manipulations of dataIs there an option to define some or all structures to be held in-memory only. Similar to partitioning, replication in ClickHouse is “static and specific” rather than “cloud style”, i. e. several servers know that they are replicas of each other (for some specific table; for a different table, replication configuration may be different). Build cloud-native applications faster with CQL, REST and GraphQL APIs. Row store means that like relational databases, Cassandra organizes data by rows and columns. ????? In fact, among the three systems discussed here, Druid offers the most to enable really cheap installations, see the section “Tiering of Query Processing Nodes in Druid” below.Because of their fundamental architectural similarity, ClickHouse, Druid and Pinot have approximately the same “limit” of efficiency and performance optimization. Both Druid and Pinot have “first class” out-of-the-box support for Hadoop. New users do not require restarting the server or redefining the security. It allows analysis of data that is updated in real time. ClickHouse vs. Druid or Pinot: Conclusions “Segmented” approach to data management in Druid and Pinot versus simpler data management in ClickHouse define … Also, Druid and Pinot are in the better position to optimize for infrastructure costs of large clusters, and better suited for the cloud environments, than ClickHouse.The only sustainable difference between Druid and Pinot is that Pinot depends on Helix framework and going to continue to depend on ZooKeeper, while Druid could move away from the dependency on ZooKeeper. The Cassandra Query Language (CQL) is a close relative of SQL.Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. ?? Those two factors helped Druid to implement “tiering” of query processing nodes: old data is automatically moved to servers with relatively larger disks, but less memory and CPU, that allows to significantly reduce costs of running a large Druid cluster, at the expense of slowing queries to older data.This feature allows Metamarkets to save hundreds of thousands dollars of Druid infrastructure spend per month, versus it had a “flat” cluster.As far as I know, ClickHouse and Pinot don’t have similar features yet, all nodes in their clusters are supposed to be the same.Since the architecture of Pinot is very similar to Druid’s, I think it would be not very hard to introduce a similar feature in Pinot. It’s too wasteful to allocate so much memory on all query processing nodes. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets.
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