We will demonstrate that in the next section. 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. 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. Executor): Key condition: (column 0 in ['http://public_search', Executor): Found (LEFT) boundary mark: 644, Executor): Found (RIGHT) boundary mark: 683, 39/1083 marks by primary key, 39 marks to read from 1 ranges, Executor): Reading approx. Is the amplitude of a wave affected by the Doppler effect? are organized into 1083 granules, as a result of the table's DDL statement containing the setting index_granularity (set to its default value of 8192). 319488 rows with 2 streams, 73.04 MB (340.26 million rows/s., 3.10 GB/s. And that is very good for the compression ratio of the content column, as a compression algorithm in general benefits from data locality (the more similar the data is the better the compression ratio is). In order to illustrate that, we give some details about how the generic exclusion search works. 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. Processed 8.87 million rows, 15.88 GB (84.73 thousand rows/s., 151.64 MB/s. In traditional relational database management systems, the primary index would contain one entry per table row. When using ReplicatedMergeTree, there are also two additional parameters, identifying shard and replica. When choosing primary key columns, follow several simple rules: Technical articles on creating, scaling, optimizing and securing big data applications, Data-intensive apps engineer, tech writer, opensource contributor @ github.com/mrcrypster. Allowing to have different primary keys in different parts of table is theoretically possible, but introduce many difficulties in query execution. The last granule (granule 1082) "contains" less than 8192 rows. Primary key allows effectively read range of data. Predecessor key column has low(er) cardinality. The primary index of our table with compound primary key (UserID, URL) was very useful for speeding up a query filtering on UserID. The following is showing ways for achieving that. mark 1 in the diagram above thus indicates that the UserID values of all table rows in granule 1, and in all following granules, are guaranteed to be greater than or equal to 4.073.710. I did found few examples in the documentation where primary keys are created by passing parameters to ENGINE section. In this case it would be likely that the same UserID value is spread over multiple table rows and granules and therefore index marks. For tables with wide format and without adaptive index granularity, ClickHouse uses .mrk mark files as visualised above, that contain entries with two 8 byte long addresses per entry. This ultimately prevents ClickHouse from making assumptions about the maximum URL value in granule 0. 'http://public_search') very likely is between the minimum and maximum value stored by the index for each group of granules resulting in ClickHouse being forced to select the group of granules (because they might contain row(s) matching the query). . The following diagram shows how the (column values of) 8.87 million rows of our table The specific URL value that the query is looking for (i.e. Similar to data files, there is one mark file per table column. The primary index of our table with compound primary key (URL, UserID) was speeding up a query filtering on URL, but didn't provide much support for a query filtering on UserID. The primary index file is completely loaded into the main memory. For. KeyClickHouse. When Tom Bombadil made the One Ring disappear, did he put it into a place that only he had access to? 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. Instead of saving all values, it saves only a portion making primary keys super small. For our example query, ClickHouse used the primary index and selected a single granule that can possibly contain rows matching our query. It is specified as parameters to storage engine. Pick only columns that you plan to use in most of your queries. Connect and share knowledge within a single location that is structured and easy to search. And because of that is is also unlikely that cl values are ordered (locally - for rows with the same ch value). tokenbf_v1ngrambf_v1String . What screws can be used with Aluminum windows? 2. 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. the same compound primary key (UserID, URL) for the index. Searching an entry in a B(+)-Tree data structure has average time complexity of O(log2 n). Why this is necessary for this example will become apparent. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. server reads data with mark ranges [0, 3) and [6, 8). 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. ClickHouse is an open-source column-oriented database developed by Yandex. The uncompressed data size of all rows together is 733.28 MB. With URL as the first column in the primary index, ClickHouse is now running binary search over the index marks. Predecessor key column has high(er) cardinality. In parallel, ClickHouse is doing the same for granule 176 for the URL.bin data file. '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. This index design allows for the primary index to be small (it can, and must, completely fit into the main memory), whilst still significantly speeding up query execution times: especially for range queries that are typical in data analytics use cases. In total, the tables data and mark files and primary index file together take 207.07 MB on disk. 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). Allow to modify primary key and perform non-blocking sorting of whole table in background. 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. For a table of 8.87 million rows, this means 23 steps are required to locate any index entry. If in a column, similar data is placed close to each other, for example via sorting, then that data will be compressed better. If the file is larger than the available free memory space then ClickHouse will raise an error. ClickHouse is an open-source column-oriented DBMS (columnar database management system) for online analytical processing (OLAP) that allows users to generate analytical reports using SQL queries in real-time. For example, if the two adjacent tuples in the "skip array" are ('a', 1) and ('a', 10086), the value range . 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. 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