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TiFlash Query Result Materialization

This document introduces how to save the TiFlash query result to a specified TiDB table in an INSERT INTO SELECT transaction.

Starting from v6.5.0, TiDB supports saving TiFlash query results in a table, that is, TiFlash query result materialization. During the execution of the INSERT INTO SELECT statement, if TiDB pushes down the SELECT subquery to TiFlash, the TiFlash query result can be saved to a TiDB table specified in the INSERT INTO clause. For TiDB versions earlier than v6.5.0, the TiFlash query results are read-only, so if you want to save TiFlash query results, you have to obtain them from the application level, and then save them in a separate transaction or process.

The syntax of INSERT INTO SELECT is as follows.

INSERT [LOW_PRIORITY | HIGH_PRIORITY] [IGNORE]
    [INTO] tbl_name
    [PARTITION (partition_name [, partition_name] ...)]
    [(col_name [, col_name] ...)]
    SELECT ...
    [ON DUPLICATE KEY UPDATE assignment_list]value:
    {expr | DEFAULT}

assignment:
    col_name = valueassignment_list:
    assignment [, assignment] ...

For example, you can save the query result from table t1 in the SELECT clause to table t2 using the following INSERT INTO SELECT statement:

INSERT INTO t2 (name, country)
SELECT app_name, country FROM t1;

  • Efficient BI solutions

    For many BI applications, the analysis query requests are very heavy. For example, when a lot of users access and refresh a report at the same time, a BI application needs to handle a lot of concurrent query requests. To deal with this situation effectively, you can use INSERT INTO SELECT to save the query results of the report in a TiDB table. Then, the end users can query data directly from the result table when the report is refreshed, which avoids multiple repeated computations and analyses. Similarly, by saving historical analysis results, you can further reduce the computation volume for long-time historical data analysis. For example, if you have a report A that is used to analyze daily sales profit, you can save the results of report A to a result table T using INSERT INTO SELECT. Then, when you need to generate a report B to analyze the sales profit of the past month, you can directly use the daily analysis results in table T. This way not only greatly reduces the computation volume but also improves the query response speed and reduces the system load.

  • Serving online applications with TiFlash

    The number of concurrent requests supported by TiFlash depends on the volume of data and complexity of the queries, but it typically does not exceed 100 QPS. You can use INSERT INTO SELECT to save TiFlash query results, and then use the query result table to support highly concurrent online requests. The data in the result table can be updated in the background at a low frequency (for example, at an interval of 0.5 second), which is well below the TiFlash concurrency limit, while still maintaining a high level of data freshness.

Execution process

  • During the execution of the INSERT INTO SELECT statement, TiFlash first returns the query results of the SELECT clause to a TiDB server in the cluster, and then writes the results to the target table, which can have a TiFlash replica.
  • The execution of the INSERT INTO SELECT statement guarantees ACID properties.

Restrictions

  • TiDB has no hard limit on the concurrency of the INSERT INTO SELECT statement, but it is recommended to consider the following practices:

    • When a "write transaction" is large, such as close to 1 GiB, it is recommended to control concurrency to no more than 10.
    • When a "write transaction" is small, such as less than 100 MiB, it is recommended to control concurrency to no more than 30.
    • Determine the concurrency based on testing results and specific circumstances.

Example

Data definition:

CREATE TABLE detail_data (
    ts DATETIME,                -- Fee generation time
    customer_id VARCHAR(20),    -- Customer ID
    detail_fee DECIMAL(20,2));  -- Amount of fee

CREATE TABLE daily_data (
    rec_date DATE,              -- Date when data is collected
    customer_id VARCHAR(20),    -- Customer ID
    daily_fee DECIMAL(20,2));   -- Amount of fee for per day

ALTER TABLE detail_data SET TIFLASH REPLICA 1;
ALTER TABLE daily_data SET TIFLASH REPLICA 1;

-- ... (detail_data table continues updating)
INSERT INTO detail_data(ts,customer_id,detail_fee) VALUES
('2023-1-1 12:2:3', 'cus001', 200.86),
('2023-1-2 12:2:3', 'cus002', 100.86),
('2023-1-3 12:2:3', 'cus002', 2200.86),
('2023-1-4 12:2:3', 'cus003', 2020.86),
('2023-1-5 12:2:3', 'cus003', 1200.86),
('2023-1-6 12:2:3', 'cus002', 20.86),
('2023-1-7 12:2:3', 'cus004', 120.56),
('2023-1-8 12:2:3', 'cus005', 320.16);

-- Execute the following SQL statement 13 times to insert a cumulative total of 65,536 rows into the table.
INSERT INTO detail_data SELECT * FROM detail_data;

Save daily analysis results:

SET @@sql_mode='NO_ZERO_IN_DATE,NO_ZERO_DATE,ERROR_FOR_DIVISION_BY_ZERO';

INSERT INTO daily_data (rec_date, customer_id, daily_fee)
SELECT DATE(ts), customer_id, sum(detail_fee) FROM detail_data WHERE DATE(ts) > DATE('2023-1-1 12:2:3') GROUP BY DATE(ts), customer_id;

Analyze monthly data based on daily analysis data:

SELECT MONTH(rec_date), customer_id, sum(daily_fee) FROM daily_data GROUP BY MONTH(rec_date), customer_id;

The preceding example materializes the daily analysis results and saves them to the daily result table, based on which the monthly data analysis is accelerated, thus improving data analysis efficiency.

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