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Data models

You must specify a data model and define one or more columns as a sort key at table creation. This way, when data is initially loaded into the table that you created, StarRocks can sort, process, and store the data based on the sort key. This topic describes the data models that StarRocks provides to meet your varying business requirements.

Basic concepts

Data models

StarRocks provides four data models: Duplicate Key, Aggregate Key, Unique Key, and Primary Key. These four data models are well suited to a wide range of data analytics scenarios such as log analysis, data aggregation and analysis, and real-time data analysis.

Sort keys

When you create a table, you can specify one or more columns based on which StarRocks sorts, processes, and stores the data loaded in to that table. The one or more columns that you specified comprise the sort key. These columns are referred to as sort key columns. The sort key is usually created on dimension columns that are frequently used as filter conditions for queries, because this can accelerate queries. In the Duplicate Key model, the sort key is created on columns that are used to sort data, and is defined by using the DUPLICATE KEY keyword. In the Aggregate Key model, the sort key is created on columns that are used to aggregate data, and is defined by using the AGGREGATE KEY keyword. In the Unique Key model or Primary Key model, the sort key is created on columns on which unique constraints are enforced, and is defined by using the PRIMARY KEY or UNIQUE KEY keyword.

Compared with traditional primary keys, sort keys in StarRocks have the following characteristics:

  • Sort keys are usually created on dimension columns that are frequently used as filter conditions for queries.

  • In the Duplicate Key model, sort keys do not need to be created on columns on which unique constraints are enforced. In the Aggregate Key model, Unique Key model, and Primary Key model, however, sort keys must be created on columns on which unique constraints are enforced.

  • StarRocks tables use clustered storage. This means that the values in each column of a table are stored in sorted order based on the sort key that you specified for the table.

  • Prefix indexes can be generated based on sort keys.

Precautions

  • Sort key columns must be defined prior to the other columns in the statement for table creation.

  • The order of sort key columns in the statement for table creation specifies the order of the conditions based on which the rows in the table are sorted.

  • The length of the prefix index for a table is limited to 36 bytes. If the total length of the sort key columns exceeds 36 bytes, StarRocks stores only the first few sort key columns within the length limit for the prefix index.

  • If the records to be loaded into a table have the same primary key, StarRocks processes and stores the records based on the data model of the table:

    • Duplicate Key model

      StarRocks loads each of the records as a separate row into the table. After the data load is complete, the table contains rows that have the same primary key, and the rows map the source records in a one-to-one relationship. You can recall all historical data that you loaded.

    • Aggregate Key model

      StarRocks aggregates the records into one record and loads the aggregated record as a row into the table. After the loading is complete, the table does not contain rows that have the same primary key. You can recall the aggregation results of all historical data that you loaded. However, you cannot recall all historical data.

    • Unique Key model and Primary Key model

      StarRocks replaces each newly loaded record with the previously loaded record and retains only the most recently loaded record as a row in the table. After the loading is complete, the table does not contain rows that have the same primary key. The Unique Key model and the Primary Key model can be considered a special Aggregate Key model in which the REPLACE aggregate function is specified for metric columns to return the most recent record among a group of records that have the same primary key.

Duplicate Key model

The Duplicate Key model is the default data model at the creation of tables in StarRocks.

When you create a table that uses the Duplicate Key model, you can define a sort key for that table. If the filter conditions for queries contain the sort key columns, StarRocks can quickly filter the data of the table to accelerate the queries. The Duplicate Key model allows you to append new data to the table. However, it does not allow you to modify the existing data in the table.

Scenarios

The Duplicate Key model is suitable for the following scenarios:

  • Analyze raw data, such as raw logs and raw operation records.
  • Query data by using various methods without the need to be limited to preaggregate methods.
  • Load log data or time series data. New data is written in append-only mode, and existing data never changes.

Create a table

Suppose that you want to analyze the event data over a specific time range. In this example, create a table named detail and define event_time and event_type as sort key columns.

The statement for creating the table is as follows:

CREATE TABLE IF NOT EXISTS detail (

    event_time DATETIME NOT NULL COMMENT "datetime of event",

    event_type INT NOT NULL COMMENT "type of event",

    user_id INT COMMENT "id of user",

    device_code INT COMMENT "device code",

    channel INT COMMENT ""

)

DUPLICATE KEY(event_time, event_type)

DISTRIBUTED BY HASH(user_id) BUCKETS 8;

Usage notes

  • Take note of the following points about the sort key of a table:

    • You can use the DUPLICATE KEY keyword to explicitly define the columns that participate in the sort key.

      Note: By default, if you do not define sort key columns, StarRocks selects the first three columns as sort key columns.

    • In the Duplicate Key model, the sort key can be composed of some or all columns.

  • You can create indexes such as BITMAP indexes and Bloom Filter indexes at table creation.

  • If two identical records are loaded, the Duplicate Key model considers the two records as one record instead of two.

What to do next

After a table is created, you can use various data ingestion methods to load data into StarRocks. For information about the data ingestion methods that are supported by StarRocks, see Data import.

Note: When you load data into a table that uses the Duplicate Key model, you can only append data to the table. You cannot modify the existing data in the table.

Aggregate Key model

When you create a table that uses the Aggregate Key model, you can define sort key columns and metric columns and can specify an aggregate function for the metric columns. If the records to be loaded have the same sort key, the metric columns are aggregated. The Aggregate Key model helps reduce the amount of data that needs to be processed for queries, thereby accelerating queries.

Scenarios

The Aggregate Key model is well suited to data statistics and analytics scenarios. A few examples are as follows:

  • Help website or app providers analyze the amount of traffic and time that their users spend on a specific website or app and the total number of visits to the website or app.

  • Help advertising agencies analyze the total clicks, total views, and consumption statistics of an advertisement that they provide for their customers.

  • Help e-commerce companies analyze their annual trading data to identify the geographic bestsellers within individual quarters or months.

The data querying and ingestion in the preceding scenarios have the following characteristics:

  • Most queries are aggregate queries, such as SUM, COUNT, and MAX.
  • Raw detailed data does not need to be retrieved.
  • Historical data is not frequently updated. Only new data is appended.

Principes

Starting from data ingestion to data querying, data with the same sort key in a table that uses the Aggregate Key model is aggregated multiple times as follows:

  1. In the data ingestion phase, when data is loaded as batches into the table, each batch comprises a data version. After a data version is generated, StarRocks aggregates the data that has the same sort key in the data version.
  2. In the background compaction phase, when the files of multiple data versions that are generated at data ingestion are periodically compacted into a large file, StarRocks aggregates the data that has the same sort key in the large file.
  3. In the data query phase, StarRocks aggregates the data that has the same sort key among all data versions before it returns the query result.

The aggregate operations help reduce the amount of data that needs to be processed, thereby accelerating queries.

Suppose that you have a table that uses the Aggregate Key model and want to load the following four raw records into the table.

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DateCountryPV
2020.05.01CHN1
2020.05.01CHN2
2020.05.01USA3
2020.05.01USA4

StarRocks aggregates the four raw records into the following two records at data ingestion.

DateCountryPV
2020.05.01CHN3
2020.05.01USA7

Create a table

Suppose that you want to analyze the numbers of visits by users from different cities to different web pages. In this example, create a table named example_db.aggregate_tbl, define site_id, date, and city_code as sort key columns, define pv as a metric column, and specify the SUM function for the pv column.

The statement for creating the table is as follows:

CREATE TABLE IF NOT EXISTS example_db.aggregate_tbl (

    site_id LARGEINT NOT NULL COMMENT "id of site",

    date DATE NOT NULL COMMENT "time of event",

    city_code VARCHAR(20) COMMENT "city_code of user",

    pv BIGINT SUM DEFAULT "0" COMMENT "total page views"

)

DISTRIBUTED BY HASH(site_id) BUCKETS 8;

Usage notes

  • Take note of the following points about the sort key of a table:

    • You can use the AGGREGATE KEY keyword to explicitly define the columns that participate in the sort key.

      • If the AGGREGATE KEY keyword does not include all dimension columns, the table cannot be created.
      • By default, if you do not explicitly define sort key columns by using the AGGREGATE KEY keyword, StarRocks selects all columns except metric columns as the sort key columns.
    • The sort key must be created on columns on which unique constraints are enforced. It must be composed of all dimension columns whose names cannot be changed.

  • You can specify an aggregate function following the name of a column to define the column as a metric column. In most cases, metric columns hold data that needs to be aggregated and analyzed.

  • For information about the aggregate functions that are supported by the Aggregate Key model, see CREATE TABLE.

  • When queries are run, sort key columns are filtered before the aggregation of multiple data versions, whereas metric columns are filtered after the aggregation of multiple data versions. Therefore, we recommend that you identify the columns that are frequently used as filter conditions and define these columns as the sort key. This way, data filtering can start before the aggregation of multiple data versions to improve query performance.

  • When you create a table, you cannot create BITMAP indexes or Bloom Filter indexes on the metric columns of the table.

What to do next

After a table is created, you can use various data ingestion methods to load data into StarRocks. For information about the data ingestion methods that are supported by StarRocks, see Data import.

Note: When you load data into a table that uses the Aggregate Key model, you can only update all columns of the table. For example, when you update the preceding example_db.aggregate_tbl table, you must update all its columns, which are site_id, date, city_code, and pv.

Unique Key model

When you create a table, you can define primary key columns and metric columns. This way, queries return the most recent record among a group of records that have the same primary key. Compared with the Duplicate Key model, the Unique Key model simplifies the data loading process to better support real-time and frequent data updates.

Scenarios

The Unique Key model is suitable for business scenarios in which data needs to be frequently updated in real time. For example, in e-commerce scenarios, hundreds of millions of orders can be placed per day, and the statuses of the orders frequently change.

Principes

The Unique Key model can be considered a special Aggregate Key model in which the REPLACE aggregate function is specified for metric columns to return the most recent record among a group of records that have the same primary key.

When you load data into a table that uses the Unique Key model, the data is split into multiple batches. Each batch is assigned a version number. Therefore, records with the same primary key may come in multiple versions, of which the most recent version (namely, the record with the largest version number) is retrieved for queries.

As shown in the following table, ID is the primary key column, value is a metric column, and _version holds the data version numbers generated within StarRocks. In this example, the record with an ID of 1 is loaded by two batches whose version numbers are 1 and 2, and the record with an ID of 2 is loaded by three batches whose version numbers are 3, 4, and 5.

IDvalue_version
11001
11012
21003
21014
21025

When you query the record with an ID of 1, the most recent record with the largest version number, which is 2 in this case, is returned. When you query the record with an ID of 2, the most recent record with the largest version number, which is 5 in this case, is returned. The following table shows the records returned by the two queries:

IDvalue
1101
2102

Create a table

In e-commerce scenarios, you often need to collect and analyze the statuses of orders by date. In this example, create a table named orders to hold the orders, define create_time and order_id, which are frequently used as conditions to filter the orders, as primary key columns, and define the other two columns, order_state and total_price, as metric columns. This way, the orders can be updated in real time as their statuses change, and can be quickly filtered to accelerate queries.

The statement for creating the table is as follows:

CREATE TABLE IF NOT EXISTS orders (

    create_time DATE NOT NULL COMMENT "create time of an order",

    order_id BIGINT NOT NULL COMMENT "id of an order",

    order_state INT COMMENT "state of an order",

    total_price BIGINT COMMENT "price of an order"

)

UNIQUE KEY(create_time, order_id)

DISTRIBUTED BY HASH(order_id) BUCKETS 8;

Usage notes

  • Take note of the following points about the primary key of a table:

    • The primary key is defined by using the UNIQUE KEY keyword.
    • The primary key must be created on columns on which unique constraints are enforced and whose names cannot be changed.
    • The primary key must be properly designed:
      • When queries are run, primary key columns are filtered before the aggregation of multiple data versions, whereas metric columns are filtered after the aggregation of multiple data versions. Therefore, we recommend that you identify the columns that are frequently used as filter conditions and define these columns as primary key columns. This way, data filtering can start before the aggregation of multiple data versions to improve query performance.
      • During the aggregation process, StarRocks compares all primary key columns. This is time-consuming and may decrease query performance. Therefore, do not define a large number of primary key columns. If a column is rarely used as a filter condition for queries, we recommend that you do not define the column as a primary key column.
  • When you create a table, you cannot create BITMAP indexes or Bloom Filter indexes on the metric columns of the table.

  • The Unique Key model does not support materialized views.

What to do next

After a table is created, you can use various data ingestion methods to load data into StarRocks. For information about the data ingestion methods that are supported by StarRocks, see Data import.

  • When you load data into a table that uses the Unique Key model, you can only update all columns of the table. For example, when you update the preceding orders table, you must update all its columns, which are create_time, order_id, order_state, and total_price.
  • When you query data from a table that uses the Unique Key model, StarRocks needs to aggregate records of multiple data versions. In this situation, a large number of data versions decreases query performance. Therefore, we recommend that you specify a proper frequency at which data is loaded into the table to meet meet your requirements for real-time data analytics while preventing a large number of data versions. If you require minute-level data, you can specify a loading frequency of 1 minute instead of a loading frequency of 1 second.

Primary Key model

StarRocks has started to support the Primary Key model since v1.19. When you create a table that uses the Primary Key model, you can define primary key columns and metric columns. Queries return the most recent record among a group of records that have the same primary key. Unlike the Unique Key model, the Primary Key model does not require aggregate operations during queries and supports the pushdown of predicates and indexes. As such, the Primary Key model can deliver high query performance despite real-time and frequent data updates.

Scenarios

  • The Primary Key model is suitable for the following scenarios in which data needs to be frequently updated in real time:

    • Stream data in real time from transaction processing systems into StarRocks. In normal cases, transaction processing systems involve a large number of update and delete operations in addition to insert operations. If you need to synchronize data from a transaction processing system to StarRocks, we recommend that you create a table that uses the Primary Key model. Then, you can use tools, such as CDC Connectors for Apache Flink®, to synchronize the binary logs of the transaction processing system to StarRocks. StarRocks uses the binary logs to add, delete, and update the data in the table in real time. This simplifies data synchronization and delivers 3 to 10 times higher query performance than when a Merge on Read (MoR) table of the Unique Key model is used. For example, you can use flink-connector-starrocks to load data. For more information, see Load data by using flink-connector-starrocks.

    • Join multiple streams by performing update operations on individual columns. In business scenarios such as user profiling, flat tables are preferably used to improve multi-dimensional analysis performance and simplify the analytics model that is used by data analysts. Upstream data in these scenarios may come from various apps, such as shopping apps, delivery apps, and banking apps, or from systems, such as machine learning systems that perform computations to obtain the distinct tags and properties of users. The Primary Key model is well suited in these scenarios, because it supports updates to individual columns. Each app or system can update only the columns that hold the data within its own service scope while benefiting from real-time data additions, deletions, and updates at high query performance.

  • The Primary Key model is suitable for scenarios in which the memory occupied by the primary key is controllable.

    The storage engine of StarRocks creates an index for the primary key of each table that uses the Primary Key model. Additionally, when you load data into a table, StarRocks loads the primary key index into the memory. Therefore, the Primary Key model requires a larger memory capacity than the other three data models. StarRocks limits the total length of the fields that comprise the primary key to 127 bytes after encoding.

    Consider using the Primary Key model if a table has the following characteristics:

    • The table contains both fast-changing data and slow-changing data. Fast-changing data is frequently updated over the most recent days, whereas slow-changing data is rarely updated. Suppose that you need to synchronize a MySQL order table to StarRocks in real time for analytics and queries. In this example, the data of the table is partitioned by day, and most updates are performed on orders that are created within the most recent days. Historical orders are no longer updated after they are completed. When you run a data load job, the primary key index is not loaded into the memory and only the index entries of the recently updated orders are loaded into the memory.

      As shown in the following figure, the data in the table is partitioned by day, and the data in the most recent two partitions is frequently updated.

      Primary index -1

    • The table is a flat table that is composed of hundreds or thousands of columns. The primary key comprises only a small portion of the table data and consumes only a small amount of memory. For example, a user status or profile table consists of a large number of columns but only tens to hundreds of millions of users. In this situation, the amount of memory consumed by the primary key is controllable.

      As shown in the following figure, the table contains only a few rows, and the primary key of the table comprises only a small portion of the table.

      Primary index -2

Principes

The Primary Key model is designed based on a new storage engine that is provided by StarRocks. The metadata structure and the read/write mechanism in the Primary Key model differ from those in the Duplicate Key model. As such, the Primary Key model does not require aggregate operations and supports the pushdown of predicates and indexes. These significantly increase query performance.

The Duplicate Key model adopts the MoR policy. MoR streamlines data writes but requires online aggregation of multiple data versions. Additionally, the Merge operator does not support the pushdown of predicates and indexes. As a result, query performance deteriorates.

The Primary Key model adopts the Delete+Insert policy to ensure that each record has a unique primary key. This way, the Primary Key model does not require merge operations. Details are as follows:

  • When StarRocks receives a request for an update operation on a record, it locates the record by searching the primary key index, marks the record as deleted, and inserts a new record. In other words, StarRocks converts an update operation to a delete operation plus an insert operation.

  • When StarRocks receives a delete operation on a record, it locates the record by searching the primary key index and marks the record as deleted.

Create a table

Example 1: Suppose that you need to analyze orders on a daily basis. In this example, create a table named orders, define dt and order_id as the primary key, and define the other columns as metric columns.

create table orders (

    dt date NOT NULL,

    order_id bigint NOT NULL,

    user_id int NOT NULL,

    merchant_id int NOT NULL,

    good_id int NOT NULL,

    good_name string NOT NULL,

    price int NOT NULL,

    cnt int NOT NULL,

    revenue int NOT NULL,

    state tinyint NOT NULL

) PRIMARY KEY (dt, order_id)

PARTITION BY RANGE(`dt`) (

    PARTITION p20210820 VALUES [('2021-08-20'), ('2021-08-21')),

    PARTITION p20210821 VALUES [('2021-08-21'), ('2021-08-22')),

    ...

    PARTITION p20210929 VALUES [('2021-09-29'), ('2021-09-30')),

    PARTITION p20210930 VALUES [('2021-09-30'), ('2021-10-01'))

) DISTRIBUTED BY HASH(order_id) BUCKETS 4

PROPERTIES("replication_num" = "3");

Example 2: Suppose that you need to analyze user behavior in real time. In this example, create a table named users, define user_id as the primary key, and define the other columns as metric columns.

create table users (

    user_id bigint NOT NULL,

    name string NOT NULL,

    email string NULL,

    address string NULL,

    age tinyint NULL,

    sex tinyint NULL,

    last_active datetime,

    property0 tinyint NOT NULL,

    property1 tinyint NOT NULL,

    property2 tinyint NOT NULL,

    property3 tinyint NOT NULL,

    ....

) PRIMARY KEY (user_id)

DISTRIBUTED BY HASH(user_id) BUCKETS 4

PROPERTIES("replication_num" = "3");

Usage notes

  • Take note of the following points about the primary key of a table:

    • The primary key is defined by using the PRIMARY KEY keyword.

    • The primary key must be created on columns on which unique constraints are enforced, and the names of the primary key columns cannot be changed.

    • The primary key columns can be any of the following data types: BOOLEAN, TINYINT, SMALLINT, INT, BIGINT, LARGEINT, STRING, VARCHAR, DATE, and DATETIME. However, the primary key columns cannot be defined as NULL.

    • The partition column and the bucket column must participate in the primary key.

    • The number and total length of primary key columns must be properly designed to save memory. We recommend that you identify columns whose data types occupy less memory and define those columns as the primary key. Such data types include INT and BIGINT. We recommend that you do not let a column of the VARCHAR data type to participate in the primary key.

    • Before you create the table, we recommend that you estimate the memory occupied by the primary key index based on the data types of the primary key columns and the number of rows in the table. This way, you can prevent the table from running out of memory. The following example explains how to calculate the memory occupied by the primary key index:

      • Suppose that the dt column, which is of the DATE data type that occupies 4 bytes, and the id column, which is of the BIGINT data type that occupies 8 bytes, are defined as the primary key. In this case, the primary key is 12 bytes in length.

      • Suppose that the table contains 10,000,000 rows of hot data and is stored in three replicas.

      • Given the preceding information, the memory occupied by the primary key index is 945 MB based on the following formula:

        (12 + 9) x 10,000,000 x 3 x 1.5 = 945 (MB)

        In the preceding formula, 9 is the immutable overhead per row, and 1.5 is the average extra overhead per hash table.

  • When you create a table, you cannot create BITMAP indexes or Bloom Filter indexes on the metric columns of the table.

  • The Primary Key model does not support materialized views.

  • You cannot use the ALTER TABLE statement to change the data types of the columns for a table that uses the Primary Key model. For the syntax and examples of using the ALTER TABLE statement, see ALTER TABLE.

What to do next

You can run a stream load, broker load, or routine load job to perform insert, update, or delete operations on all or individual columns of a table that uses the Primary Key model. For more information, see Data load into tables of Primary Key model.