EFFICIENT QUERY PROCESSING IN COLUMNAR DATABASES USING BLOOM FILTERS

• US 20150169655A1
• Filed: 03/02/2015
• Published: 06/18/2015
• Est. Priority Date: 02/21/2013
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: GUPTA, ANURAG WINDLASS
• Granted Patent: US 9,367,574 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/221 Column-oriented storage; Management thereof
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP

Abstract

A bloom filter is generated for efficient query processing for unsorted data in a column of a columnar database. Bloom filters represented as bitmaps are generated for data blocks storing data for a column of a columnar database table. An indication of a query directed toward the column is received and the bloom filter for each data block is examined to determine which ones of the data blocks do not need to be read in order to service the query for the select data. Data is then read from the data blocks storing data for the column excepting the ones which do not need to be read.

Claims

1-20. -20. (canceled)

21. A distributed data warehouse system, comprising:

a plurality of nodes, wherein at least some nodes of the plurality of nodes each comprise;

storage for a columnar database table, wherein said storage comprises a plurality of data blocks; and

a data access module;

the data access module, configured to;

generate a probabilistic data structure for each of one or more data blocks storing data for a column of the columnar database table, wherein each probabilistic data structure indicates data values not stored in the data block;

receive an indication of a query directed to the column of the columnar database table for select data; and

in response to the receipt of the indication of the query, examine the probabilistic data structure for each of the one or more data blocks storing data for the column to determine particular ones of the one or more data blocks which do not need to be read in order to service the query for the select data.

22. The system of claim 21, wherein the data access module is further configured to:

receive additional data to be stored in an additional data block for the column of the columnar database table; and

generate an additional probabilistic data structure for the additional data block.

23. The system of claim 21, wherein to generate the probabilistic data structure for each of the one or more data blocks storing the data for the column of the columnar database table, the data access module is configured to:

generate a bitmap representing the probabilistic data structure and comprising a plurality of bits; and

populate the bitmap with the different patterns of set bits based, at least in part, on the data stored in the data block to produce the probabilistic data structure.

24. The system of claim 21, wherein the one or more nodes of the plurality of nodes are one or more compute nodes of a data warehouse cluster, wherein a different node of the plurality of nodes is a leader node of the data warehouse cluster, and wherein the leader node is configured to send one or more queries directed to the column of the columnar database table to the one or more compute nodes.

25. A method, comprising:

performing, by one or more computing devices;

generating a probabilistic data structure for each of one or more data blocks storing data for a column of a columnar database table, wherein each probabilistic data structure indicates data values not stored in the data block;

receiving an indication of a query directed to the column of the columnar database table for select data; and

in response to receiving the indication of the query, examining the probabilistic data structure for each of the one or more data blocks storing data for the column to determine particular ones of the one or more data blocks which do not need to be read in order to service the query for the select data.

26. The method of claim 25, wherein said generating the probabilistic data structure for each of the one or more data blocks storing the data for the column of the columnar database table comprises:

generating a bitmap representing the probabilistic data structure and comprising a plurality of bits; and

populating the bitmap with the different patterns of set bits based, at least in part, on the data stored in the data block to produce the probabilistic data structure.

27. The method of claim 26, wherein said examining the probabilistic data structure for each of the one or more data blocks comprises:

for a given data block;

for each data value of the select data;

determining bit pattern locations corresponding to the data value; and

examining the bit pattern locations in the bitmap representing the probabilistic data structure for the given data block to determine whether the given data block is one of the particular ones which do not need to be read in order to service the query for the select data.

28. The method of claim 25, further comprising in response to receiving the indication of the query, reading the data from the one or more data blocks storing data for the column in order to service the query for the select data excepting the particular ones of the one or more data blocks which do not need to be read.

29. The method of claim 25, wherein the data for the column of the columnar database table is unsorted.

30. The method of claim 25, further comprising for each of the one or more data blocks, storing the probabilistic data structure in a respective entry in a block metadata data structure that stores information about the one or more data blocks.

31. The method of claim 25, further comprising:

receiving additional data to be stored in one of the one or more data blocks for the column of the columnar database table; and

updating the probabilistic data structure for the one data block to include the additional data.

32. The method of claim 25, wherein the indication of the query further indicates that the query is a data join query, wherein the plurality of computing devices are part of a larger collection of computing devices implementing a database cluster in a distributed data warehouse system, wherein the plurality of computing devices implement a compute node of the database cluster, wherein another plurality of computing devices that are part of the larger collection of computing devices implement a different compute node storing another columnar database table, and wherein the method further comprises:

performing, by the compute node;

in response to receiving the indication of the query, sending the probabilistic data structure for each of at least some of the one or more data blocks to the different compute node.

performing, by the different compute node;

receiving the indication of the data join query for the select data;

receiving the probabilistic data structure for each of the at least some of the one or more data blocks from the compute node; and

in response to receiving the indication of the data join query and receiving the probabilistic data structure for the at least some of the one or more data blocks, obtaining data from data blocks storing data for the other columnar database table based, at least in part, on the probabilistic data structure for each of the at least some data blocks in order to service the data join query for the select data.

33. The method of claim 25, further comprising:

detecting an indexing event for the column of the columnar database table; and

in response to detecting the indexing event;

for each of the one or more data blocks, generating a new probabilistic data structure which indicates one or more data values not stored in the data block in place of the probabilistic data structure.

34. The method of claim 33, wherein said detecting an indexing event for the column of the columnar database table comprises:

for each of the one or more data blocks, evaluating the probabilistic data structure for the data block to determine a selectivity level for the bitmap; and

determining that the selectivity level for at least some of the one or more data blocks is below a selectivity efficiency threshold.

35. The method of claim 33, further comprising:

receiving a plurality of indications of a plurality of different queries directed to the column of the columnar database table; and

wherein said detecting an indexing event for the column of the columnar database table comprises analyzing the plurality of different queries to determine that a number of the queries are range queries and that the number of range queries exceeds a query type threshold.

36. A non-transitory, computer-readable storage medium, storing program instructions that when executed by one or more computing devices implement:

generating a probabilistic data structure for each of one or more data blocks storing data for a column of a columnar database table, wherein each probabilistic data structure indicates data values not stored in the data block;

for each of the one or more data blocks, storing the probabilistic data structure in a respective entry for the data block in a block metadata data structure that stores information about the one or more data blocks.

receiving an indication of a query directed to the column of the columnar database table for select data; and

in response to receiving the indication of the query;

analyzing the probabilistic data structure for each of the one or more data blocks storing data for the column to determine particular ones of the one or more data blocks which do not need to be read in order to service the query for the select data; and

reading the one or more data blocks storing data for the column in order to service the query for the select data excepting the particular ones of the one or more data blocks which do not need to be read.

37. The non-transitory, computer-readable storage medium of claim 36, wherein, in said generating the probabilistic data structure for each of the one or more data blocks storing data for the column of the columnar database table, the program instructions when executed by the one or more computing devices implement:

generating a bitmap representing the probabilistic data structure and comprising a plurality of bits corresponding to the probabilistic data structure size; and

populating the bitmap representing the probabilistic data structure based, at least in part, on the data values stored in the data block.

38. The non-transitory, computer-readable storage medium of claim 37, wherein, in said examining the probabilistic data structure for each of the one or more data blocks storing data for the column to determine particular ones of the one or more data blocks which do not need to be read in order to service the query for the select data, the program instructions when executed by the one or more computing devices implement:

for a given data block;

for each data value of the select data;

determining bit pattern locations according to the data value; and

examining the bit pattern locations in the bitmap representing the probabilistic data structure for the given data block to determine whether the given data block is one of the particular ones which do not need to be read in order to service the query for the select data.

39. The non-transitory, computer-readable storage medium of claim 36, wherein the program instructions when executed by the one or more computing devices further implement:

detecting an indexing event for the column of the columnar database table; and

in response to detecting the indexing event;

for each of the one or more data blocks, generating a new probabilistic data structure which indicates one or more data values not stored in the data block in place of the probabilistic data structure.

40. The non-transitory, computer-readable storage medium of claim 36, wherein the data for the column of the columnar database table is unsorted.