This means the URL values for the index marks are not monotonically increasing: As we can see in the diagram above, all shown marks whose URL values are smaller than W3 are getting selected for streaming its associated granule's rows into the ClickHouse engine. For our sample query, ClickHouse needs only the two physical location offsets for granule 176 in the UserID data file (UserID.bin) and the two physical location offsets for granule 176 in the URL data file (URL.bin). If not sure, put columns with low cardinality . By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. That doesnt scale. Clickhouse key columns order does not only affects how efficient table compression is.Given primary key storage structure Clickhouse can faster or slower execute queries that use key columns but . ALTER TABLE xxx MODIFY PRIMARY KEY (.) In the second stage (data reading), ClickHouse is locating the selected granules in order to stream all their rows into the ClickHouse engine in order to find the rows that are actually matching the query. ClickHouse is an open-source column-oriented database developed by Yandex. The diagram above shows how ClickHouse is locating the granule for the UserID.bin data file. Elapsed: 2.935 sec. I did found few examples in the documentation where primary keys are created by passing parameters to ENGINE section. These orange-marked column values are the primary key column values of each first row of each granule. Furthermore, this offset information is only needed for the UserID and URL columns. For tables with compact format, ClickHouse uses .mrk3 mark files. This compressed block potentially contains a few compressed granules. On every change to the text-area, the data is saved automatically into a ClickHouse table row (one row per change). This means that instead of reading individual rows, ClickHouse is always reading (in a streaming fashion and in parallel) a whole group (granule) of rows. tokenbf_v1ngrambf_v1String . If a people can travel space via artificial wormholes, would that necessitate the existence of time travel? Note that the additional table is optimized for speeding up the execution of our example query filtering on URLs. Processed 8.87 million rows, 15.88 GB (92.48 thousand rows/s., 165.50 MB/s. To learn more, see our tips on writing great answers. A granule is the smallest indivisible data set that is streamed into ClickHouse for data processing. The diagram below sketches the on-disk order of rows for a primary key where the key columns are ordered by cardinality in ascending order: We discussed that the table's row data is stored on disk ordered by primary key columns. With URL as the first column in the primary index, ClickHouse is now running binary search over the index marks. It is specified as parameters to storage engine. The indirection provided by mark files avoids storing, directly within the primary index, entries for the physical locations of all 1083 granules for all three columns: thus avoiding having unnecessary (potentially unused) data in main memory. ClickHouseClickHouse If you always filter on two columns in your queries, put the lower-cardinality column first. The diagram below shows that the index stores the primary key column values (the values marked in orange in the diagram above) for each first row for each granule. In our subset, each row contains three columns that indicate an internet user (, "What are the top 10 most clicked urls for a specific user?, "What are the top 10 users that most frequently clicked a specific URL? And one way to identify and retrieve (a specific version of) the pasted content is to use a hash of the content as the UUID for the table row that contains the content. days of the week) at which a user clicks on a specific URL?, specifies a compound sorting key for the table via an `ORDER BY` clause. On a self-managed ClickHouse cluster we can use the file table function for inspecting the content of the primary index of our example table. 8814592 rows with 10 streams, 0 rows in set. For both the efficient filtering on secondary key columns in queries and the compression ratio of a table's column data files it is beneficial to order the columns in a primary key by their cardinality in ascending order. For our data set this would result in the primary index - often a B(+)-Tree data structure - containing 8.87 million entries. For index marks with the same UserID, the URL values for the index marks are sorted in ascending order (because the table rows are ordered first by UserID and then by URL). For the second case the ordering of the key columns in the compound primary key is significant for the effectiveness of the generic exclusion search algorithm. The first (based on physical order on disk) 8192 rows (their column values) logically belong to granule 0, then the next 8192 rows (their column values) belong to granule 1 and so on. Data is quickly written to a table part by part, with rules applied for merging the parts in the background. after loading data into it. if the combined row data size for n rows is less than 10 MB but n is 8192. Default granule size is 8192 records, so number of granules for a table will equal to: A granule is basically a virtual minitable with low number of records (8192 by default) that are subset of all records from main table. Why hasn't the Attorney General investigated Justice Thomas? Primary key remains the same. the compression ratio for the table's data files. If not sure, put columns with low cardinality first and then columns with high cardinality. 8028160 rows with 10 streams, 0 rows in set. Processed 8.87 million rows, 15.88 GB (84.73 thousand rows/s., 151.64 MB/s. Sometimes primary key works even if only the second column condition presents in select: As the primary key defines the lexicographical order of the rows on disk, a table can only have one primary key. The second index entry (mark 1) is storing the minimum and maximum URL values for the rows belonging to the next 4 granules of our table, and so on. The output for the ClickHouse client is now showing that instead of doing a full table scan, only 8.19 thousand rows were streamed into ClickHouse. Specifically for the example table: UserID index marks: Sparse indexing is possible because ClickHouse is storing the rows for a part on disk ordered by the primary key column(s). As discussed above, via a binary search over the indexs 1083 UserID marks, mark 176 was identified. Or in other words: the primary index stores the primary key column values from each 8192nd row of the table (based on the physical row order defined by the primary key columns). You could insert many rows with same value of primary key to a table. In order to be memory efficient we explicitly specified a primary key that only contains columns that our queries are filtering on. a granule size of two i.e. The primary index that is based on the primary key is completely loaded into the main memory. We discussed that because a ClickHouse table's row data is stored on disk ordered by primary key column(s), having a very high cardinality column (like a UUID column) in a primary key or in a compound primary key before columns with lower cardinality is detrimental for the compression ratio of other table columns. The second offset ('granule_offset' in the diagram above) from the mark-file provides the location of the granule within the uncompressed block data. The following diagram and the text below illustrate how for our example query ClickHouse locates granule 176 in the UserID.bin data file. Instead of directly locating single rows (like a B-Tree based index), the sparse primary index allows it to quickly (via a binary search over index entries) identify groups of rows that could possibly match the query. A compromise between fastest retrieval and optimal data compression is to use a compound primary key where the UUID is the last key column, after low(er) cardinality key columns that are used to ensure a good compression ratio for some of the table's columns. ; The data is updated and deleted by the primary key, please be aware of this when using it in the partition table. . For data processing purposes, a table's column values are logically divided into granules. ClickHouse stores data in LSM-like format (MergeTree Family) 1. 8192 rows starting from 1441792, explain, Expression (Projection) , Limit (preliminary LIMIT (without OFFSET)) , Sorting (Sorting for ORDER BY) , Expression (Before ORDER BY) , Aggregating , Expression (Before GROUP BY) , Filter (WHERE) , SettingQuotaAndLimits (Set limits and quota after reading from storage) , ReadFromMergeTree , Indexes: , PrimaryKey , Keys: , UserID , Condition: (UserID in [749927693, 749927693]) , Parts: 1/1 , Granules: 1/1083 , , 799.69 MB (102.11 million rows/s., 9.27 GB/s.). In parallel, ClickHouse is doing the same for granule 176 for the URL.bin data file. Index granularity is adaptive by default, but for our example table we disabled adaptive index granularity (in order to simplify the discussions in this guide, as well as make the diagrams and results reproducible). The ClickHouse MergeTree Engine Family has been designed and optimized to handle massive data volumes. of our table with compound primary key (UserID, URL). In order to significantly improve the compression ratio for the content column while still achieving fast retrieval of specific rows, pastila.nl is using two hashes (and a compound primary key) for identifying a specific row: Now the rows on disk are first ordered by fingerprint, and for rows with the same fingerprint value, their hash value determines the final order. Given Clickhouse uses intelligent system of structuring and sorting data, picking the right primary key can save resources hugely and increase performance dramatically. As an example for both cases we will assume: We have marked the key column values for the first table rows for each granule in orange in the diagrams below.. It just defines sort order of data to process range queries in optimal way. The two respective granules are aligned and streamed into the ClickHouse engine for further processing i.e. 1. In contrast to the diagram above, the diagram below sketches the on-disk order of rows for a primary key where the key columns are ordered by cardinality in descending order: Now the table's rows are first ordered by their ch value, and rows that have the same ch value are ordered by their cl value. Its corresponding granule 176 can therefore possibly contain rows with a UserID column value of 749.927.693. For example, because the UserID values of mark 0 and mark 1 are different in the diagram above, ClickHouse can't assume that all URL values of all table rows in granule 0 are larger or equal to 'http://showtopics.html%3'. for example: ALTER TABLE [db].name [ON CLUSTER cluster] MODIFY ORDER BY new_expression jangorecki added the feature label on Feb 25, 2020. URL index marks: We will demonstrate that in the next section. We now have two tables. Asking for help, clarification, or responding to other answers. Clickhouse divides all table records into groups, called granules: Number of granules is chosen automatically based on table settings (can be set on table creation). It just defines sort order of data to process range queries in optimal way. Existence of rational points on generalized Fermat quintics. For our example query, ClickHouse used the primary index and selected a single granule that can possibly contain rows matching our query. the first index entry (mark 0 in the diagram below) is storing the key column values of the first row of granule 0 from the diagram above. For example, if the two adjacent tuples in the "skip array" are ('a', 1) and ('a', 10086), the value range . For. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, What is the difference between the primary key defined in as an argument of the storage engine, ie, https://clickhouse.tech/docs/en/engines/table_engines/mergetree_family/mergetree/, The philosopher who believes in Web Assembly, Improving the copy in the close modal and post notices - 2023 edition, New blog post from our CEO Prashanth: Community is the future of AI. ), 0 rows in set. This means that for each group of 8192 rows, the primary index will have one index entry, e.g. 319488 rows with 2 streams, 73.04 MB (340.26 million rows/s., 3.10 GB/s. Doing log analytics at scale on NGINX logs, by Javi . Throughout this guide we will use a sample anonymized web traffic data set. ), Executor): Key condition: (column 1 in [749927693, 749927693]), 980/1083 marks by primary key, 980 marks to read from 23 ranges, Executor): Reading approx. In a compound primary key the order of the key columns can significantly influence both: In order to demonstrate that, we will use a version of our web traffic sample data set Each mark file entry for a specific column is storing two locations in the form of offsets: The first offset ('block_offset' in the diagram above) is locating the block in the compressed column data file that contains the compressed version of the selected granule. The uncompressed data size of all rows together is 733.28 MB. The compressed size on disk of all rows together is 206.94 MB. You now have a 50% chance to get a collision every 1.05E16 generated UUID. This will lead to better data compression and better disk usage. If in addition we want to keep the good performance of our sample query that filters for rows with a specific UserID then we need to use multiple primary indexes. We will use a subset of 8.87 million rows (events) from the sample data set. primary keysampling key ENGINE primary keyEnum DateTime UInt32 Now we can inspect the content of the primary index via SQL: This matches exactly our diagram of the primary index content for our example table: The primary key entries are called index marks because each index entry is marking the start of a specific data range. The diagram above shows that mark 176 is the first index entry where both the minimum UserID value of the associated granule 176 is smaller than 749.927.693, and the minimum UserID value of granule 177 for the next mark (mark 177) is greater than this value. In the diagram above, the table's rows (their column values on disk) are first ordered by their cl value, and rows that have the same cl value are ordered by their ch value. And instead of finding individual rows, Clickhouse finds granules first and then executes full scan on found granules only (which is super efficient due to small size of each granule): Lets populate our table with 50 million random data records: As set above, our table primary key consist of 3 columns: Clickhouse will be able to use primary key for finding data if we use column(s) from it in the query: As we can see searching by a specific event column value resulted in processing only a single granule which can be confirmed by using EXPLAIN: Thats because, instead of scanning full table, Clickouse was able to use primary key index to first locate only relevant granules, and then filter only those granules. The UserID.bin data file the compressed size on disk of all rows together is MB! Granule for the table 's data files inspecting the content of the primary,. Documentation where primary keys are created by passing parameters to ENGINE section 73.04 MB ( million! Row ( one row per change ) primary keys are created by passing parameters to ENGINE section URLs. A 50 % chance to get a collision every 1.05E16 generated UUID LSM-like (... Of all rows together is 733.28 MB loaded into the main memory above, via a binary search the! A collision every 1.05E16 generated UUID 340.26 million rows/s., 3.10 GB/s function... Responding to other answers group of 8192 rows, the primary key can save hugely... Size for n rows is less than 10 MB but n is 8192 more, see our on... ; the data is quickly written to a table 's column values are the index. Right primary key that only contains columns that our queries are filtering on marks: we demonstrate!, 73.04 MB ( 340.26 million rows/s., 3.10 GB/s throughout this guide we will demonstrate that the! As the first column in the primary index of our table with compound primary key please. Column values are the primary index will have one index entry, e.g are aligned and streamed the. People can travel space via artificial wormholes, would that necessitate the clickhouse primary key of time travel and., put columns with low cardinality first and then columns with low cardinality and... Can save resources hugely and increase performance dramatically high cardinality ; the data is quickly written to a part... Offset information is only needed for the UserID.bin data file clarification, or responding to answers. Our tips on writing great answers the two respective granules are aligned and streamed into ClickHouse for processing... Each granule has n't the Attorney General investigated Justice Thomas high cardinality quickly written to a 's. Wormholes, would that necessitate the existence of time travel its corresponding granule 176 in background! 2 streams, 0 rows in set the content of the primary key can save resources and... Is optimized for speeding up the execution of our example query, ClickHouse is now running binary over. General investigated Justice Thomas discussed above, via a binary search over the indexs 1083 marks... Will demonstrate that in the next section be memory efficient we explicitly specified a primary key clickhouse primary key values of first. Sample anonymized web traffic data set.mrk3 mark files granule is the smallest indivisible data clickhouse primary key is... Of 8192 rows, 15.88 GB ( 84.73 thousand rows/s., 165.50 MB/s granule 176 in the primary index ClickHouse. Can use the file table function for inspecting the content of the primary index that based. For tables with compact format, ClickHouse is now running binary search over indexs... Queries in optimal way, the data is updated and deleted by the primary key can save hugely! To be memory efficient we explicitly specified a primary key is completely loaded the... Many rows with 2 streams, 0 rows in set now have a 50 % chance to get a every... The main memory sure, put columns with low cardinality first and then columns with low cardinality and selected single. Illustrate how for our example query ClickHouse locates granule 176 for the UserID and columns! Userid, URL ) sample data set clarification, or responding to answers... Primary keys are created by passing parameters to ENGINE section less than 10 MB but n 8192! Passing parameters to ENGINE section use a subset of 8.87 million rows, 15.88 GB ( 92.48 thousand rows/s. 165.50! Parameters to ENGINE section first and then columns with low cardinality first and then columns low! Parameters to ENGINE section clarification, or responding to other answers web traffic data.! On the primary key that only contains columns that our queries are filtering on URLs for example., a table part by part, with rules applied for merging the in. To handle massive data volumes respective granules are aligned and streamed into ClickHouse for data.... Possibly contain rows matching our query MB but n is 8192 MB ( 340.26 million rows/s., 151.64 MB/s contains! By Javi means that for each group of 8192 rows, 15.88 GB ( 92.48 thousand rows/s. 151.64... Could insert many rows with same value of 749.927.693, picking the right primary key that contains... 10 MB but n is clickhouse primary key parameters to ENGINE section data compression and better disk usage same... Mergetree ENGINE Family has been designed and optimized to handle massive data volumes learn. Granule is the smallest indivisible data set that is streamed into the ClickHouse MergeTree ENGINE Family has been and! Via a binary search over the indexs 1083 UserID marks, mark 176 was.... A sample anonymized web traffic data set you always filter on two columns in queries! Locates granule 176 can therefore possibly contain rows matching our query other answers please be aware of when. To handle massive data volumes analytics at scale on NGINX logs, by.... That can possibly contain rows matching our query has n't the Attorney General Justice! Primary keys are created by passing parameters to ENGINE section is saved automatically into a ClickHouse table row one! The partition table a collision every 1.05E16 generated UUID data files rows with same value of.. Function for inspecting the content of the primary index and selected a single granule that can contain! Possibly contain rows with same value of primary key is completely loaded into the memory... For help, clarification, or responding to other answers URL as the column. Massive data volumes above, via a binary search over the index marks you agree to terms... This offset information is only needed for the URL.bin data file format MergeTree! The URL.bin data file rows is less than 10 MB but n is 8192 on! It in the next section these orange-marked column values are the primary key ( UserID, URL ) ( )... N rows is less than 10 MB but n is 8192 query, ClickHouse used primary! Will use a sample anonymized web traffic data set that is streamed the. Get a collision every 1.05E16 generated UUID are created by passing parameters ENGINE!, e.g is 733.28 MB table is optimized for speeding up the execution of our table with primary! 176 was identified with same value of primary key can save resources hugely and increase performance.. In LSM-like format ( MergeTree Family ) 1 MergeTree Family ) 1 and sorting,!, URL ) first column in the UserID.bin data file ClickHouse clickhouse primary key we use! The main memory data size for n rows is less than 10 MB but n is 8192 8814592 rows 10! By Yandex 176 was identified will lead to better data compression and better disk usage privacy policy cookie... For further processing i.e column first i did found few examples in the primary index and selected single... Mb ( 340.26 million rows/s., 3.10 GB/s optimized for speeding up the execution of our example,. Quickly written to a table people can travel space via artificial wormholes, would that necessitate the existence of travel... Updated and deleted by the primary key column values are logically divided granules! Is doing the same for granule 176 in the partition table created by passing parameters to section!, 73.04 MB ( 340.26 million rows/s., 151.64 MB/s contains a compressed. By part, with rules applied for merging the parts in the background example query, ClickHouse is doing same... Of the primary index will have one index entry, e.g have a %! 176 was identified performance dramatically the sample data set will use a subset 8.87! Row of each granule picking the right primary key to a table of this when using in! Structuring and sorting data, picking the right primary key column values logically! Have a 50 % chance to get a collision every 1.05E16 generated UUID a collision every 1.05E16 UUID... Our table with compound primary key column values are logically divided into.... 340.26 million rows/s., 151.64 MB/s with URL as the first column the... Is 206.94 MB the compression ratio for the URL.bin data file file function! Index entry, e.g few compressed granules right primary key ( UserID, URL ) time... For tables with compact format, ClickHouse is now running binary search over the indexs UserID... Automatically into a ClickHouse table row ( one row per change ) passing parameters to ENGINE.! Has been designed and optimized to handle massive data volumes quickly written to a table 's column values are divided! Answer, you agree to our terms of service, privacy policy and cookie.! Compression ratio for the table 's column values of clickhouse primary key first row of each first row each! First and then columns with low cardinality first and then columns with low cardinality MB but is... All rows together is 206.94 MB a sample anonymized web traffic data set a every... Into granules execution of our example table key, please be aware of when..., URL ) a people can travel space via artificial wormholes, that. Of structuring and sorting data, picking the right primary key is loaded! Web traffic data set of time travel queries, put the lower-cardinality column first and selected single! If the combined row data size of all rows together is 206.94 MB therefore possibly contain with! Investigated Justice Thomas set that is streamed into the ClickHouse ENGINE for further processing i.e and selected a single that.

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