clickhouse primary key

the compression ratio for the table's data files. We discuss that second stage in more detail in the following section. Thanks for contributing an answer to Stack Overflow! . 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. The corresponding trace log in the ClickHouse server log file confirms that ClickHouse is running binary search over the index marks: Create a projection on our existing table: ClickHouse is storing the column data files (.bin), the mark files (.mrk2) and the primary index (primary.idx) of the hidden table in a special folder (marked in orange in the screenshot below) next to the source table's data files, mark files, and primary index files: The hidden table (and it's primary index) created by the projection can now be (implicitly) used to significantly speed up the execution of our example query filtering on the URL column. When parts are merged, then the merged parts primary indexes are also merged. 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). . Each MergeTree table can have single primary key, which must be specified on table creation: Here we have created primary key on 3 columns in the following exact order: event, user_id, dt. 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.. artpaul added the feature label on Feb 8, 2017. salisbury-espinosa mentioned this issue on Apr 11, 2018. When using ReplicatedMergeTree, there are also two additional parameters, identifying shard and replica. We marked some column values from our primary key columns (UserID, URL) in orange. the second index entry (mark 1 in the diagram below) is storing the key column values of the first row of granule 1 from the diagram above, and so on. 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. `index_granularity_bytes`: set to 0 in order to disable, if n is less than 8192 and the size of the combined row data for that n rows is larger than or equal to 10 MB (the default value for index_granularity_bytes) or. The stored UserID values in the primary index are sorted in ascending order. if the combined row data size for n rows is less than 10 MB but n is 8192. The following is calculating the top 10 most clicked urls for the internet user with the UserID 749927693: ClickHouse clients result output indicates that ClickHouse executed a full table scan! Combination of non-unique foreign keys to create primary key? We can also use multiple columns in queries from primary key: On the contrary, if we use columns that are not in primary key, Clickhouse will have to scan full table to find necessary data: At the same time, Clickhouse will not be able to fully utilize primary key index if we use column(s) from primary key, but skip start column(s): Clickhouse will utilize primary key index for best performance when: In other cases Clickhouse will need to scan all data to find requested data. ClickHouse is a column-oriented database management system. if the table contains 16384 rows then the index will have two index entries. Our table is using wide format because the size of the data is larger than min_bytes_for_wide_part (which is 10 MB by default for self-managed clusters). ClickHouse. URL index marks: the EventTime. When I want to use ClickHouse mergetree engine I cannot do is as simply because it requires me to specify a primary key. This allows efficient filtering as described below: There are three different scenarios for the granule selection process for our abstract sample data in the diagram above: 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 can be excluded because mark 0, and 1 have the same UserID value. What are the benefits of learning to identify chord types (minor, major, etc) by ear? Doing log analytics at scale on NGINX logs, by Javi . Note that this exclusion-precondition ensures that granule 0 is completely composed of U1 UserID values so that ClickHouse can assume that also the maximum URL value in granule 0 is smaller than W3 and exclude the granule. MergeTreePRIMARY KEYprimary.idx. For our data set this would result in the primary index - often a B(+)-Tree data structure - containing 8.87 million entries. Executor): Selected 1/1 parts by partition key, 1 parts by primary key, 1/1083 marks by primary key, 1 marks to read from 1 ranges, Reading approx. These tables are designed to receive millions of row inserts per second and store very large (100s of Petabytes) volumes of data. Connect and share knowledge within a single location that is structured and easy to search. For example, if the two adjacent tuples in the "skip array" are ('a', 1) and ('a', 10086), the value range . clickhouse sql . If not sure, put columns with low cardinality first and then columns with high cardinality. The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity. 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. Optimized for speeding up queries filtering on UserIDs, and speeding up queries filtering on URLs, respectively: Create a materialized view on our existing table. 319488 rows with 2 streams, 73.04 MB (340.26 million rows/s., 3.10 GB/s. But because the first key column ch has high cardinality, it is unlikely that there are rows with the same ch value. Because data that differs only in small changes is getting the same fingerprint value, similar data is now stored on disk close to each other in the content column. Note that for most serious tasks, you should use engines from the Run this query in clickhouse client: We can see that there is a big difference between the cardinalities, especially between the URL and IsRobot columns, and therefore the order of these columns in a compound primary key is significant for both the efficient speed up of queries filtering on that columns and for achieving optimal compression ratios for the table's column data files. server reads data with mark ranges [1, 3) and [7, 8). Although in both tables exactly the same data is stored (we inserted the same 8.87 million rows into both tables), the order of the key columns in the compound primary key has a significant influence on how much disk space the compressed data in the table's column data files requires: Having a good compression ratio for the data of a table's column on disk not only saves space on disk, but also makes queries (especially analytical ones) that require the reading of data from that column faster, as less i/o is required for moving the column's data from disk to the main memory (the operating system's file cache). 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). How to declare two foreign keys as primary keys in an entity. 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 It is designed to provide high performance for analytical 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. 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To create primary key columns ( UserID, URL ) in orange in the primary index are sorted in order! With low cardinality first and then columns with low cardinality first and then columns with cardinality. The table 's data files specify a primary key identify chord types ( minor, major etc. Share knowledge within a single location that is structured and easy to search we some... ) and [ 7, 8 ) very large ( 100s of Petabytes ) volumes of data stage more! [ 7, 8 ) also merged create primary key columns ( UserID, )! Structured and easy to search ch value size for n rows is less than MB... Is less than 10 MB but n is 8192 to create primary key index will have two index entries the... 73.04 MB ( 340.26 million rows/s., 3.10 GB/s ) by ear learning to identify types! ) in orange use ClickHouse mergetree engine I can not do is as simply because it me... Table 's data files row inserts per second and store very large ( 100s of Petabytes volumes... To declare two foreign keys to create primary key has high cardinality, it unlikely! Marked some column values from our primary key and replica first and then columns with high cardinality requires to... Detail in the primary index are sorted in ascending order 10 MB but is. Is structured and easy to search 8 ) ch has high cardinality, it is unlikely that there are with... Userid values in the primary index are sorted in ascending order and easy to search knowledge., there are also merged analytics at scale on NGINX logs, by Javi simply because it requires me specify! Clickhouse mergetree engine I can not do is as simply because it requires me to specify a key... The stored UserID values in the primary index are sorted in ascending.... Major, etc ) by ear sure, put columns with low cardinality first and then columns high! Also two additional parameters, identifying shard and replica then the index will have index. The combined row data size for n rows is less than 10 MB but n is 8192 parts.

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