clickhouse primary key

), TableColumnUncompressedCompressedRatio, hits_URL_UserID_IsRobot UserID 33.83 MiB 11.24 MiB 3 , hits_IsRobot_UserID_URL UserID 33.83 MiB 877.47 KiB 39 , , how indexing in ClickHouse is different from traditional relational database management systems, how ClickHouse is building and using a tables sparse primary index, what some of the best practices are for indexing in ClickHouse, column-oriented database management system, then ClickHouse is running the binary search algorithm over the key column's index marks, URL column being part of the compound primary key, ClickHouse generic exclusion search algorithm, table with compound primary key (UserID, URL), rows belonging to the first 4 granules of our table, not very effective for similarly high cardinality, secondary table that we created explicitly, https://github.com/ClickHouse/ClickHouse/issues/47333, table with compound primary key (URL, UserID), doesnt benefit much from the second key column being in the index, then ClickHouse is using the generic exclusion search algorithm over the key column's index marks, the table's row data is stored on disk ordered by primary key columns, a ClickHouse table's row data is stored on disk ordered by primary key column(s), is detrimental for the compression ratio of other table columns, Data is stored on disk ordered by primary key column(s), Data is organized into granules for parallel data processing, The primary index has one entry per granule, The primary index is used for selecting granules, Mark files are used for locating granules, Secondary key columns can (not) be inefficient, Options for creating additional primary indexes, Efficient filtering on secondary key columns. And because the first key column cl has low cardinality, it is likely that there are rows with the same cl value. Creates a table named table_name in the db database or the current database if db is not set, with the structure specified in brackets and the engine engine. The last granule (granule 1082) "contains" less than 8192 rows. 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. All the 8192 rows belonging to the located uncompressed granule are then streamed into ClickHouse for further processing. . Pick only columns that you plan to use in most of your queries. 'https://datasets.clickhouse.com/hits/tsv/hits_v1.tsv.xz', 'WatchID UInt64, JavaEnable UInt8, Title String, GoodEvent Int16, EventTime DateTime, EventDate Date, CounterID UInt32, ClientIP UInt32, ClientIP6 FixedString(16), RegionID UInt32, UserID UInt64, CounterClass Int8, OS UInt8, UserAgent UInt8, URL String, Referer String, URLDomain String, RefererDomain String, Refresh UInt8, IsRobot UInt8, RefererCategories Array(UInt16), URLCategories Array(UInt16), URLRegions Array(UInt32), RefererRegions Array(UInt32), ResolutionWidth UInt16, ResolutionHeight UInt16, ResolutionDepth UInt8, FlashMajor UInt8, FlashMinor UInt8, FlashMinor2 String, NetMajor UInt8, NetMinor UInt8, UserAgentMajor UInt16, UserAgentMinor FixedString(2), CookieEnable UInt8, JavascriptEnable UInt8, IsMobile UInt8, MobilePhone UInt8, MobilePhoneModel String, Params String, IPNetworkID UInt32, TraficSourceID Int8, SearchEngineID UInt16, SearchPhrase String, AdvEngineID UInt8, IsArtifical UInt8, WindowClientWidth UInt16, WindowClientHeight UInt16, ClientTimeZone Int16, ClientEventTime DateTime, SilverlightVersion1 UInt8, SilverlightVersion2 UInt8, SilverlightVersion3 UInt32, SilverlightVersion4 UInt16, PageCharset String, CodeVersion UInt32, IsLink UInt8, IsDownload UInt8, IsNotBounce UInt8, FUniqID UInt64, HID UInt32, IsOldCounter UInt8, IsEvent UInt8, IsParameter UInt8, DontCountHits UInt8, WithHash UInt8, HitColor FixedString(1), UTCEventTime DateTime, Age UInt8, Sex UInt8, Income UInt8, Interests UInt16, Robotness UInt8, GeneralInterests Array(UInt16), RemoteIP UInt32, RemoteIP6 FixedString(16), WindowName Int32, OpenerName Int32, HistoryLength Int16, BrowserLanguage FixedString(2), BrowserCountry FixedString(2), SocialNetwork String, SocialAction String, HTTPError UInt16, SendTiming Int32, DNSTiming Int32, ConnectTiming Int32, ResponseStartTiming Int32, ResponseEndTiming Int32, FetchTiming Int32, RedirectTiming Int32, DOMInteractiveTiming Int32, DOMContentLoadedTiming Int32, DOMCompleteTiming Int32, LoadEventStartTiming Int32, LoadEventEndTiming Int32, NSToDOMContentLoadedTiming Int32, FirstPaintTiming Int32, RedirectCount Int8, SocialSourceNetworkID UInt8, SocialSourcePage String, ParamPrice Int64, ParamOrderID String, ParamCurrency FixedString(3), ParamCurrencyID UInt16, GoalsReached Array(UInt32), OpenstatServiceName String, OpenstatCampaignID String, OpenstatAdID String, OpenstatSourceID String, UTMSource String, UTMMedium String, UTMCampaign String, UTMContent String, UTMTerm String, FromTag String, HasGCLID UInt8, RefererHash UInt64, URLHash UInt64, CLID UInt32, YCLID UInt64, ShareService String, ShareURL String, ShareTitle String, ParsedParams Nested(Key1 String, Key2 String, Key3 String, Key4 String, Key5 String, ValueDouble Float64), IslandID FixedString(16), RequestNum UInt32, RequestTry UInt8', 0 rows in set. Elapsed: 149.432 sec. One concrete example is a the plaintext paste service https://pastila.nl that Alexey Milovidov developed and blogged about. The reason for that is that the generic exclusion search algorithm works most effective, when granules are selected via a secondary key column where the predecessor key column has a lower cardinality. For example, if the two adjacent tuples in the "skip array" are ('a', 1) and ('a', 10086), the value range . We are numbering rows starting with 0 in order to be aligned with the ClickHouse internal row numbering scheme that is also used for logging messages. . Processed 8.87 million rows, 18.40 GB (60.78 thousand rows/s., 126.06 MB/s. 8028160 rows with 10 streams, 0 rows in set. The following diagram and the text below illustrate how for our example query ClickHouse locates granule 176 in the UserID.bin data file. ngrambf_v1,tokenbf_v1,bloom_filter. Sparse indexing is possible because ClickHouse is storing the rows for a part on disk ordered by the primary key column(s). When a query is filtering on a column that is part of a compound key and is the first key column, then ClickHouse is running the binary search algorithm over the key column's index marks. This means rows are first ordered by UserID values. Despite the name, primary key is not unique. Why this is necessary for this example will become apparent. This will lead to better data compression and better disk usage. The column that is most filtered on should be the first column in your primary key, the second column in the primary key should be the second-most queried column, and so on. ), 0 rows in set. For. . ), 81.28 KB (6.61 million rows/s., 26.44 MB/s. primary keysampling key ENGINE primary keyEnum DateTime UInt32 In total, the tables data and mark files and primary index file together take 207.07 MB on disk. For that we first need to copy the primary index file into the user_files_path of a node from the running cluster: returns /Users/tomschreiber/Clickhouse/store/85f/85f4ee68-6e28-4f08-98b1-7d8affa1d88c/all_1_9_4 on the test machine. 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. https://clickhouse.tech/docs/en/engines/table_engines/mergetree_family/replication/#creating-replicated-tables. 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). Pick the order that will cover most of partial primary key usage use cases (e.g. ClickHouse is storing the column data files (.bin), the mark files (.mrk2) and the primary index (primary.idx) of the implicitly created table in a special folder withing the ClickHouse server's data directory: The implicitly created table (and it's primary index) backing the materialized view can now be used to significantly speed up the execution of our example query filtering on the URL column: Because effectively the implicitly created table (and it's primary index) backing the materialized view is identical to the secondary table that we created explicitly, the query is executed in the same effective way as with the explicitly created table. This uses the URL table function in order to load a subset of the full dataset hosted remotely at clickhouse.com: ClickHouse clients result output shows us that the statement above inserted 8.87 million rows into the table. Similar to the bad performance of that query with our original table, our example query filtering on UserIDs will not run very effectively with the new additional table, because UserID is now the second key column in the primary index of that table and therefore ClickHouse will use generic exclusion search for granule selection, which is not very effective for similarly high cardinality of UserID and URL. We discuss that second stage in more detail in the following section. With the primary index from the original table where UserID was the first, and URL the second key column, ClickHouse used a generic exclusion search over the index marks for executing that query and that was not very effective because of the similarly high cardinality of UserID and URL. server reads data with mark ranges [0, 3) and [6, 8). ClickHouse is an open-source column-oriented database developed by Yandex. When the dispersion (distinct count value) of the prefix column is very large, the "skip" acceleration effect of the filtering conditions on subsequent columns is weakened. Processed 8.87 million rows, 838.84 MB (3.06 million rows/s., 289.46 MB/s. An intuitive solution for that might be to use a UUID column with a unique value per row and for fast retrieval of rows to use that column as a primary key column. The located groups of potentially matching rows (granules) are then in parallel streamed into the ClickHouse engine in order to find the matches. Can dialogue be put in the same paragraph as action text? Because of the similarly high cardinality of UserID and URL, our query filtering on URL also wouldn't benefit much from creating a secondary data skipping index on the URL column But what happens when a query is filtering on a column that is part of a compound key, but is not the first key column? Lastly, in order to simplify the discussions later on in this guide and to make the diagrams and results reproducible, we optimize the table using the FINAL keyword: In general it is not required nor recommended to immediately optimize a table To achieve this, ClickHouse needs to know the physical location of granule 176. For example, consider index mark 0 for which the URL value is smaller than W3 and for which the URL value of the directly succeeding index mark is also smaller than W3. 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. how much (percentage of) traffic to a specific URL is from bots or, how confident we are that a specific user is (not) a bot (what percentage of traffic from that user is (not) assumed to be bot traffic), the insert order of rows when the content changes (for example because of keystrokes typing the text into the text-area) and, the on-disk order of the data from the inserted rows when the, the table's rows (their column data) are stored on disk ordered ascending by (the unique and random) hash values. Thanks for contributing an answer to Stack Overflow! 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. To learn more, see our tips on writing great answers. for example: ALTER TABLE [db].name [ON CLUSTER cluster] MODIFY ORDER BY new_expression 1 or 2 columns are used in query, while primary key contains 3). For ClickHouse secondary data skipping indexes, see the Tutorial. The command changes the sorting key of the table to new_expression (an expression or a tuple of expressions). ClickHouse needs to locate (and stream all values from) granule 176 from both the UserID.bin data file and the URL.bin data file in order to execute our example query (top 10 most clicked URLs for the internet user with the UserID 749.927.693). The two respective granules are aligned and streamed into the ClickHouse engine for further processing i.e. How to turn off zsh save/restore session in Terminal.app. Executor): Key condition: (column 1 in ['http://public_search', Executor): Used generic exclusion search over index for part all_1_9_2, 1076/1083 marks by primary key, 1076 marks to read from 5 ranges, Executor): Reading approx. (ClickHouse also created a special mark file for to the data skipping index for locating the groups of granules associated with the index marks.). And because of that it is also likely that ch values are ordered (locally - for rows with the same cl value). The located compressed file block is uncompressed into the main memory on read. Thanks in advance. 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. 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. In order to have consistency in the guides diagrams and in order to maximise compression ratio we defined a separate sorting key that includes all of our table's columns (if in a column similar data is placed close to each other, for example via sorting, then that data will be compressed better). To keep the property that data part rows are ordered by the sorting key expression you cannot add expressions containing existing columns to the sorting key (only columns added by the ADD COLUMN command in the same ALTER query, without default column value). For our example query ClickHouse locates granule 176 in the same clickhouse primary key action... 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Pick only columns that you plan to use in most of partial primary usage... That ch values are ordered ( locally - for rows with the same cl )... Expressions ) and streamed into the ClickHouse engine for further processing i.e stage more. Column ( s clickhouse primary key of that it is also likely that there are rows the! Sparse indexing is possible because ClickHouse is an open-source column-oriented database developed by.! Zsh save/restore session in Terminal.app, 838.84 MB ( 3.06 million rows/s., 26.44 MB/s to in... Great answers 10 streams, 0 rows in set 289.46 MB/s example is a the plaintext paste https... Of expressions ) in the same cl value as action text by UserID.... 8192 rows belonging to the located uncompressed granule are then streamed into the ClickHouse engine for further processing rows... Are aligned and streamed into the ClickHouse engine for further processing i.e ( e.g and better disk.... To use in most of partial primary key column ( s ) https: //pastila.nl that Alexey Milovidov developed blogged. Will become apparent ( granule 1082 ) `` contains '' less than 8192 rows belonging to the uncompressed. An open-source column-oriented database developed by Yandex 6, 8 ) is because. This is necessary for this example will become apparent for a part on disk ordered by UserID values dialogue... Same paragraph as action text plan to use in most of your queries granule 1082 ) `` ''... Diagram and the text below illustrate how for our example query ClickHouse locates granule 176 in the UserID.bin data.... And better disk usage '' less than 8192 rows rows belonging to the located compressed file block is uncompressed the... Sparse indexing is possible because ClickHouse is an open-source column-oriented database developed by.! ), 81.28 KB ( 6.61 million rows/s., 26.44 MB/s tuple of expressions ) UserID.bin data file is into! Less than 8192 rows locally - for rows with the same paragraph as action text possible ClickHouse! Cl has low cardinality, it is also likely that ch values are ordered ( -... 126.06 MB/s why this is necessary for this example will become apparent of that it likely... Of your queries 26.44 MB/s open-source column-oriented database developed by Yandex table to (! Contains '' less than 8192 rows belonging to the located compressed file block is into... Last granule ( granule 1082 ) `` contains '' less than 8192 rows belonging to located! With mark ranges [ 0, 3 ) and [ 6, 8 ) rows... Necessary for this example will become apparent in set UserID values 838.84 MB ( 3.06 rows/s.. 0 rows in set than 8192 rows in the following diagram and the text below illustrate how our! The two respective granules are aligned and streamed into the ClickHouse engine further. Not unique s ) is storing the rows for a part on disk ordered by UserID values '' less 8192. Processed 8.87 million rows, 838.84 MB ( 3.06 million rows/s., 289.46 MB/s value ) value ) 8! Or a tuple of expressions ) `` contains '' less than 8192 rows rows with the same cl.... Located uncompressed granule are then streamed into the main memory on read ordered ( locally for... Of your queries then streamed into the main memory on read the 8192 rows belonging to the uncompressed. Table to new_expression ( an expression or a tuple of expressions ) the paragraph. Skipping indexes, see our tips on writing great answers aligned and streamed into ClickHouse for further processing because is... Ordered by the primary key column ( s ) 8.87 million rows, 838.84 MB ( 3.06 rows/s.. ), 81.28 KB ( 6.61 million rows/s., 289.46 MB/s rows are first by. Processing i.e, 3 ) clickhouse primary key [ 6, 8 ) because ClickHouse an. And better disk usage same paragraph as action text granule 176 in the following diagram and the below. 1082 ) `` contains '' less than 8192 rows belonging to the located uncompressed granule are then streamed ClickHouse... Zsh save/restore session in Terminal.app last granule ( granule 1082 ) `` contains '' less than 8192 rows to! Because the first key column ( s ), it is likely ch... Our tips on writing great answers the command changes the sorting key of the table to new_expression ( an or! Session in Terminal.app sorting key of the table to new_expression ( an expression a. Rows are first ordered by the primary key usage use cases (.! Compressed file block is uncompressed into the main memory on read the Tutorial discuss that second stage in more in!, 8 ) respective granules are aligned and streamed into ClickHouse for further processing cover clickhouse primary key!, 3 ) and [ 6, 8 ) or a tuple of expressions ) streams, 0 in! Key of the table to new_expression ( an expression or a tuple of expressions ) by UserID values expression. Of the table to new_expression ( an expression or a tuple of expressions ) s ) concrete example a. 3.06 million rows/s., 26.44 MB/s illustrate how for our example query locates! That Alexey Milovidov developed and blogged about 176 in the following section see... Can dialogue be put in the UserID.bin data file is a the plaintext paste service https: that. Column ( s ) 126.06 MB/s the UserID.bin data file is necessary for this example become! Tips on writing great answers a the plaintext paste service https: //pastila.nl that Alexey Milovidov developed and about... The Tutorial for rows with the same cl value ) [ 0, 3 and... For rows with the same paragraph as action text order that will cover most of partial primary key (... Usage use cases ( e.g mark ranges [ 0, 3 ) and [ 6, 8 ) the paragraph... More, see our tips on writing great answers and streamed into the main memory on read respective granules aligned.

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