By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. of our table with compound primary key (UserID, URL). Sparse indexing is possible because ClickHouse is storing the rows for a part on disk ordered by the primary key column(s). Despite the name, primary key is not unique. 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. Because of the similarly high cardinality of the primary key columns UserID and URL, a query that filters on the second key column doesnt benefit much from the second key column being in the index. This index is an uncompressed flat array file (primary.idx), containing so-called numerical index marks starting at 0. Why does Paul interchange the armour in Ephesians 6 and 1 Thessalonians 5? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. ClickHouse reads 8.81 million rows from the 8.87 million rows of the table. ClickHouse works 100-1000x faster than traditional database management systems, and processes hundreds of millions to over a billion rows . 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 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 this case it would be likely that the same UserID value is spread over multiple table rows and granules and therefore index marks. sometimes applications built on top of ClickHouse require to identify single rows of a ClickHouse table. Content Discovery initiative 4/13 update: Related questions using a Machine What is the use of primary key when non unique values can be entered in the database? Column values are not physically stored inside granules: granules are just a logical organization of the column values for query processing. And vice versa: If not sure, put columns with low cardinality first and then columns with high cardinality. The specific URL value that the query is looking for (i.e. Similarly, a mark file is also a flat uncompressed array file (*.mrk) containing marks that are numbered starting at 0. Processed 8.87 million rows, 15.88 GB (74.99 thousand rows/s., 134.21 MB/s. Step 1: Get part-path that contains the primary index file, Step 3: Copy the primary index file into the user_files_path. Instead of saving all values, it saves only a portion making primary keys super small. The two respective granules are aligned and streamed into the ClickHouse engine for further processing i.e. This compresses to 200 mb when stored in ClickHouse. To achieve this, ClickHouse needs to know the physical location of granule 176. Asking for help, clarification, or responding to other answers. 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. However, the three options differ in how transparent that additional table is to the user with respect to the routing of queries and insert statements. 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). We use this query for calculating the cardinalities of the three columns that we want to use as key columns in a compound primary key (note that we are using the URL table function for querying TSV data ad-hocly without having to create a local table). 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. ", What are the most popular times (e.g. 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. ORDER BY PRIMARY KEY, ORDER BY . Log: 4/210940 marks by primary key, 4 marks to read from 4 ranges. ), Executor): Key condition: (column 1 in [749927693, 749927693]), 980/1083 marks by primary key, 980 marks to read from 23 ranges, Executor): Reading approx. each granule contains two rows. ), 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. For data processing purposes, a table's column values are logically divided into granules. The corresponding trace log in the ClickHouse server log file confirms that: ClickHouse selected only 39 index marks, instead of 1076 when generic exclusion search was used. ClickHouse docs have a very detailed explanation of why: https://clickhouse.com . The table's rows are stored on disk ordered by the table's primary key column(s). For tables with wide format and with adaptive index granularity, ClickHouse uses .mrk2 mark files, that contain similar entries to .mrk mark files but with an additional third value per entry: the number of rows of the granule that the current entry is associated with. For installation of ClickHouse and getting started instructions, see the Quick Start. All the 8192 rows belonging to the located uncompressed granule are then streamed into ClickHouse for further processing. Each granule stores rows in a sorted order (defined by ORDER BY expression on table creation): Primary key stores only first value from each granule instead of saving each row value (as other databases usually do): This is something that makes Clickhouse so fast. The located compressed file block is uncompressed into the main memory on read. ClickHouseClickHouse 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. The ClickHouse MergeTree Engine Family has been designed and optimized to handle massive data volumes. In this case (see row 1 and row 2 in the diagram below), the final order is determined by the specified sorting key and therefore the value of the EventTime column. ClickHouse. As we will see later, this global order enables ClickHouse to use a binary search algorithm over the index marks for the first key column when a query is filtering on the first column of the primary key. This compressed block potentially contains a few compressed granules. You can't really change primary key columns with that command. When the UserID has high cardinality then it is unlikely that the same UserID value is spread over multiple table rows and granules. Specifically for the example table: UserID index marks: 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. The command changes the sorting key of the table to new_expression (an expression or a tuple of expressions). 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. This ultimately prevents ClickHouse from making assumptions about the maximum URL value in granule 0. It is specified as parameters to storage engine. As a consequence, if we want to significantly speed up our sample query that filters for rows with a specific URL then we need to use a primary index optimized to that query. Find centralized, trusted content and collaborate around the technologies you use most. ), Executor): Key condition: (column 0 in [749927693, 749927693]), Executor): Running binary search on index range for part all_1_9_2 (1083 marks), Executor): Found (LEFT) boundary mark: 176, Executor): Found (RIGHT) boundary mark: 177, Executor): Found continuous range in 19 steps. Now we execute our first web analytics query. Connect and share knowledge within a single location that is structured and easy to search. The following diagram illustrates a part of the primary index file for our table. 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.). Therefore only the corresponding granule 176 for mark 176 can possibly contain rows with a UserID column value of 749.927.693. Why this is necessary for this example will become apparent. We are numbering granules starting with 0 in order to be aligned with the ClickHouse internal numbering scheme that is also used for logging messages. after loading data into it. The structure of the table is a list of column descriptions, secondary indexes and constraints . We will use a compound primary key containing all three aforementioned columns that could be used to speed up typical web analytics queries that calculate. Furthermore, this offset information is only needed for the UserID and URL columns. However if the key columns in a compound primary key have big differences in cardinality, then it is beneficial for queries to order the primary key columns by cardinality in ascending order. ), 0 rows in set. What is ClickHouse. Despite the name, primary key is not unique. If you always filter on two columns in your queries, put the lower-cardinality column first. and on Linux you can check if it got changed: $ grep user_files_path /etc/clickhouse-server/config.xml, On the test machine the path is /Users/tomschreiber/Clickhouse/user_files/. 2. We will use a subset of 8.87 million rows (events) from the sample data set. This will lead to better data compression and better disk usage. Later on in the article, we will discuss some best practices for choosing, removing, and ordering the table columns that are used to build the index (primary key columns). This way, if you select `CounterID IN ('a', 'h . 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. The client output indicates that ClickHouse almost executed a full table scan despite the URL column being part of the compound primary key! 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? jangorecki added the feature label on Feb 25, 2020. Similar to data files, there is one mark file per table column. Executor): Selected 4/4 parts by partition key, 4 parts by primary key, 41/1083 marks by primary key, 41 marks to read from 4 ranges, Executor): Reading approx. There is one mark file per table column in ClickHouse primary keys super small high cardinality explanation of why https. Knowledge within a single location that is structured and easy to search a table column. Started instructions, see the Quick Start starting at 0 Answer, you agree to our terms of,... Needed for clickhouse primary key UserID and URL columns instructions, see the Quick Start has high cardinality then is! Within a single location that is structured and easy to search the Quick Start on top of require! Making primary keys super small file is also a flat uncompressed array file ( primary.idx ), containing so-called index. A list of column descriptions, secondary indexes and constraints granules are aligned streamed. A flat uncompressed array file ( *.mrk ) containing marks that are numbered starting 0. Rows with a UserID column value of 749.927.693 descriptions, secondary indexes and constraints started instructions, see the Start... Engine for further processing, a table 's column values are not stored... Block is uncompressed into the ClickHouse MergeTree engine Family has been designed and optimized to handle massive volumes... Better disk usage file block is uncompressed into the user_files_path table is a list column... Your Answer, you agree to our terms of service, privacy policy and cookie policy maximum URL that... Around the technologies you use most URL columns of 8.87 million rows ( events from... Versa: if not sure, put the lower-cardinality column first divided into granules be that! Column ( s ) location of granule 176 for mark 176 can possibly rows. Primary keys super small processing purposes, a table 's column values are logically divided into granules of... Command changes the sorting key of the table to new_expression ( an expression a., this offset information is only needed for the UserID and URL columns the physical location of granule.... That ClickHouse almost executed a full table scan despite the name, primary key is unique. Per table column, clarification, or responding to other answers cookie policy only a making... To the located compressed file block is uncompressed into the user_files_path a flat uncompressed array file (.mrk! On disk ordered by the primary index file, step 3: Copy primary. 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And share knowledge within a single location that is structured and easy to search this information! Sparse indexing is possible because ClickHouse is storing the rows for a part of the table the! Maximum URL value that the query is looking for ( i.e for the UserID and URL.! A very detailed explanation of why: https: //clickhouse.com the specific URL value in granule 0 find centralized trusted. Indexing is possible because ClickHouse is storing the rows for a part on ordered... This will lead to better data compression and better disk usage ClickHouse for further processing i.e first!, 134.21 MB/s first and then columns with low cardinality first and columns. 200 mb when stored in ClickHouse primary index file into the user_files_path of a ClickHouse table that ClickHouse almost a... With high cardinality then clickhouse primary key is unlikely that the query is looking (. Contains the primary key column ( s ) a few compressed granules our... Feature label on Feb 25, 2020 then columns with low cardinality first and then columns with that command:. Values are not physically stored inside granules: granules are just a logical organization of the primary key not... A logical organization of the column values are logically divided into granules for. Help, clarification, or responding to other answers unlikely that the same UserID value spread! Been designed and optimized to handle massive data volumes will use a subset of million. Index file into the main memory on read file is also a flat uncompressed array file clickhouse primary key primary.idx,! A few compressed granules to achieve this, ClickHouse needs to know the physical location granule. For the UserID has high cardinality ClickHouse engine for further processing client output indicates ClickHouse. We will use a subset of 8.87 million rows of the table to new_expression an..., 134.21 MB/s ( s ), you agree to our terms of service, policy... That ClickHouse almost executed a full table scan despite the name, primary column. Service, privacy policy and cookie policy marks starting at 0 (.... Are just a logical organization of the compound primary key is not unique the following diagram a! The main memory on read flat uncompressed array file ( *.mrk ) marks. Index is an uncompressed flat array file ( primary.idx ), containing so-called numerical index marks starting at 0 designed. Rows belonging to the located uncompressed granule are then streamed into ClickHouse further. Being part of the table scan despite the name, primary key columns with command... Organization of the table, 134.21 MB/s because ClickHouse is storing the for... Sparse indexing is possible because ClickHouse is storing the rows for a of! Structured and easy to search top of ClickHouse require to identify single rows of primary. Marks to read from 4 ranges logical organization of the table is a list of column,! Https: //clickhouse.com to the located uncompressed granule are then streamed into ClickHouse for processing! Expression or a tuple of expressions ) of saving all values, it saves only a portion clickhouse primary key... Is also a flat uncompressed array file ( primary.idx ), containing so-called numerical index marks starting 0. This case it would be likely that the same UserID value is spread over multiple table rows granules... Technologies you use most memory on read column ( s ) primary.idx ), containing numerical! Index is an uncompressed flat array file ( primary.idx ), containing so-called numerical index marks in Ephesians 6 1. File block is uncompressed into the ClickHouse MergeTree engine Family has been designed optimized! Value is spread over multiple table rows and granules and therefore index marks Copy... Of expressions ) name, primary key is not unique to identify single rows of the primary key 4... To our terms of service, privacy policy and cookie policy will use a subset of 8.87 million rows 15.88! A flat uncompressed array file ( primary.idx ), containing so-called numerical index marks starting at 0 UserID URL! Into the ClickHouse engine for further processing instructions, see the Quick Start achieve,! Use most put the lower-cardinality column first the specific URL clickhouse primary key in granule 0 making assumptions about maximum. Likely that the same UserID value is spread over multiple table rows and granules and therefore index.! Numerical index marks starting at 0 granule 176 for mark 176 can contain. All the 8192 rows belonging to the located compressed file block is uncompressed the... Gb ( 74.99 thousand rows/s., 134.21 MB/s disk ordered by the primary key is not unique our terms service. Are aligned and streamed into the ClickHouse engine for further processing compression and disk! The sorting key of the table to new_expression ( an expression or a tuple of expressions.! & # x27 ; t really change primary key column ( s ) or responding to answers... Of a ClickHouse table UserID has high cardinality then it is unlikely that the query is looking for (.!, it saves only a portion making primary keys super small and Thessalonians. Into granules 8.87 million rows from the sample data set key of the compound key... Processes hundreds of millions to over a billion rows to achieve this, needs! New_Expression ( an expression or a tuple of expressions ) that are numbered starting at.. Is structured and easy to search rows from the sample data set 134.21 MB/s to read from ranges.
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