![]() That's why it's good to use this ordering column in the filtering expressions of the query.Īnother type of supported index is Clustered Index that stores data rows ordered by the index key. The CCI can also be ordered to ensure that every segment contains a not overlapping subset of data meaning that it will be either fully skipped or read. Large tables with more than 100 million rows can take full advantage of the compression. ![]() It can then rely on column compression to optimize the storage and querying (segment skipped if it doesn't contain relevant data to the query). It's composed of a rowgroup where Synapse stores table data in a column-oriented storage. In addition to the distributions, Synapse also has a concept of indexes, which is not obvious too! By default, Synapse with create a Clustered Columnstore Index (CCI). Below you can find an example of a hash-distributed table on a user_id column, partitioned by user_activity date: Put another way, the partitions will be distributed. If the table is also partitioned, each distribution will be responsible for distinct partitions. Let's take an example of a hash-distributed table. It concerns the round-robin and hash distribution strategies because the replicated is stored on the compute node. Synapse stores a distributed table across 60 distributions. It took me a while to understand the difference, so let me share it here. In Synapse, you can also use partitions and indexes. Unfortunately, it's not the single aspect. Of course, if we analyzed the data model only with this data distribution aspect, it would be too easy. It distributes the records evenly so that every node gets approximately the same volume of data.
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