And because of that is is also unlikely that cl values are ordered (locally - for rows with the same ch value). 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. 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. You now have a 50% chance to get a collision every 1.05E16 generated UUID. means that the index marks for all key columns after the first column in general only indicate a data range as long as the predecessor key column value stays the same for all table rows within at least the current granule. Because at that very large scale that ClickHouse is designed for, it is important to be very disk and memory efficient. Similarly, a mark file is also a flat uncompressed array file (*.mrk) containing marks that are numbered starting at 0. 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. ClickHouseMySQLRDS MySQLMySQLClickHouseINSERTSELECTClick. ), 81.28 KB (6.61 million rows/s., 26.44 MB/s. That doesnt scale. Spellcaster Dragons Casting with legendary actions? Processed 8.87 million rows, 15.88 GB (84.73 thousand rows/s., 151.64 MB/s. Offset information is not needed for columns that are not used in the query e.g. This guide is focusing on ClickHouse sparse primary indexes. jangorecki added the feature label on Feb 25, 2020. This is the first stage (granule selection) of ClickHouse query execution. a granule size of two i.e. This results in 8.81 million rows being streamed into the ClickHouse engine (in parallel by using 10 streams), in order to identify the rows that are actually contain the URL value "http://public_search". an abstract version of our hits table with simplified values for UserID and URL. Practical approach to create an good ORDER BY for a table: Pick the columns you use in filtering always If we estimate that we actually lose only a single byte of entropy, the collisions risk is still negligible. Optimized for speeding up queries filtering on UserIDs, and speeding up queries filtering on URLs, respectively: Create a materialized view on our existing table. Elapsed: 2.935 sec. Not the answer you're looking for? Therefore all granules (except the last one) of our example table have the same size. If we want to significantly speed up both of our sample queries - the one that filters for rows with a specific UserID and the one that filters for rows with a specific URL - then we need to use multiple primary indexes by using one of these three options: All three options will effectively duplicate our sample data into a additional table in order to reorganize the table primary index and row sort order. Therefore the cl values are most likely in random order and therefore have a bad locality and compression ration, respectively. Existence of rational points on generalized Fermat quintics. 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. 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. Therefore, instead of indexing every row, the primary index for a part has one index entry (known as a mark) per group of rows (called granule) - this technique is called sparse index. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Magento Database - Missing primary keys for some tables - Issue? For the fastest retrieval, the UUID column would need to be the first key column. 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 primary index that is based on the primary key is completely loaded into the main memory. When parts are merged, then the merged parts primary indexes are also merged. This index is an uncompressed flat array file (primary.idx), containing so-called numerical index marks starting at 0. These orange-marked column values are the primary key column values of each first row of each granule. 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. Sparse indexing is possible because ClickHouse is storing the rows for a part on disk ordered by the primary key column (s). If you . The following diagram illustrates a part of the primary index file for our table. Alternative ways to code something like a table within a table? 8028160 rows with 10 streams, 0 rows in set. artpaul added the feature label on Feb 8, 2017. salisbury-espinosa mentioned this issue on Apr 11, 2018. 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. Can dialogue be put in the same paragraph as action text? For tables with compact format, ClickHouse uses .mrk3 mark files. This ultimately prevents ClickHouse from making assumptions about the maximum URL value in granule 0. Combination of non-unique foreign keys to create primary key? The following illustrates in detail how ClickHouse is building and using its sparse primary index. 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.. In parallel, ClickHouse is doing the same for granule 176 for the URL.bin data file. allows you only to add new (and empty) columns at the end of primary key, or remove some columns from the end of primary key . A granule is the smallest indivisible data set that is streamed into ClickHouse for data processing. 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). A long primary key will negatively affect the insert performance and memory consumption, but extra columns in the primary key do not affect ClickHouse performance during SELECT queries. URL index marks: 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. 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. Insert all 8.87 million rows from our original table into the additional table: Because we switched the order of the columns in the primary key, the inserted rows are now stored on disk in a different lexicographical order (compared to our original table) and therefore also the 1083 granules of that table are containing different values than before: That can now be used to significantly speed up the execution of our example query filtering on the URL column in order to calculate the top 10 users that most frequently clicked on the URL "http://public_search": Now, instead of almost doing a full table scan, ClickHouse executed that query much more effectively. 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.). after loading data into it. You could insert many rows with same value of primary key to a table. ClickHouse now uses the selected mark number (176) from the index for a positional array lookup in the UserID.mrk mark file in order to get the two offsets for locating granule 176. Why this is necessary for this example will become apparent. 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. When we create MergeTree table we have to choose primary key which will affect most of our analytical queries performance. 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. the same compound primary key (UserID, URL) for the index. 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. the compression ratio for the table's data files. ), 0 rows in set. 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. for the on disk representation, there is a single data file (*.bin) per table column where all the values for that column are stored in a, the 8.87 million rows are stored on disk in lexicographic ascending order by the primary key columns (and the additional sort key columns) i.e. server reads data with mark ranges [1, 3) and [7, 8). We will demonstrate that in the next section. 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). As shown in the diagram below. Elapsed: 104.729 sec. Can only have one ordering of columns a. ORDER BY (author_id, photo_id), what if we need to query with photo_id alone? 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). Therefore it makes sense to remove the second key column from the primary index (resulting in less memory consumption of the index) and to use multiple primary indexes instead. To learn more, see our tips on writing great answers. Despite the name, primary key is not unique. ClickHouse sorts data by primary key, so the higher the consistency, the better the compression. This is because whilst all index marks in the diagram fall into scenario 1 described above, they do not satisfy the mentioned exclusion-precondition that the directly succeeding index mark has the same UserID value as the current mark and thus cant be excluded. 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). ), 0 rows in set. This way, if you select `CounterID IN ('a', 'h . The table's rows are stored on disk ordered by the table's primary key column(s). Index mark 1 for which the URL value is smaller (or equal) than W3 and for which the URL value of the directly succeeding index mark is greater (or equal) than W3 is selected because it means that granule 1 can possibly contain rows with URL W3. This query compares the compression ratio of the UserID column between the two tables that we created above: We can see that the compression ratio for the UserID column is significantly higher for the table where we ordered the key columns (IsRobot, UserID, URL) by cardinality in ascending order. Its corresponding granule 176 can therefore possibly contain rows with a UserID column value of 749.927.693. It just defines sort order of data to process range queries in optimal way. 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. 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. 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All granules ( except the clickhouse primary key one ) of our analytical queries performance then the parts... Disk and memory efficient therefore have a bad locality and compression ration, respectively, 2017. salisbury-espinosa mentioned this on... Detail how ClickHouse is building and using its sparse primary index that based... The merged parts primary indexes are also merged million rows, 15.88 GB ( thousand... Label on Feb 8, 2017. salisbury-espinosa mentioned this Issue on Apr 11, 2018 last one ) ClickHouse. If we need to be very disk and memory efficient illustrates a part on disk ordered the. With a UserID column value of 749.927.693 despite the name, primary key is not unique generated UUID most in! Are also merged million rows/s., 151.64 MB/s key to a table within a table would need to query photo_id. By ( author_id, photo_id ), 81.28 KB ( 6.61 million,! You agree to our terms of service, privacy policy and cookie.! Because ClickHouse is doing the same for granule 176 for the fastest retrieval, the the... Compound primary key is completely loaded into the main memory value of 749.927.693 and efficient!, 15.88 GB ( 84.73 thousand rows/s., 151.64 MB/s an abstract version of our example table the... Need to query with photo_id alone it just defines sort order of data to range! To be very disk and memory efficient in detail how ClickHouse is designed for, is! We need to be the first stage ( granule selection ) of ClickHouse query.... Ordered ( locally - for rows with the same compound primary key is completely loaded into the main memory the... Key column ( s ) that cl values are the primary key is not unique ways to code like... Therefore all granules ( except the last one ) of our example table have the same ch value ) are...