---

{ “title”: “Updating Data with the UPDATE Command”, “language”: “en”, “description”: “Use the UPDATE command on Doris Unique Key Model tables to modify data, supporting small-scale row-level updates and field-level ETL batch updates.” }

This document describes how to use the UPDATE command in Apache Doris to modify data in tables that use the Unique Key Model, including applicable scenarios, how it works, performance factors, and a complete usage example.

The UPDATE command applies only to tables that use the Unique Key Model.

Applicable Scenarios

The UPDATE command is recommended for the following two typical scenarios:

Scenario	Description	Example
Small-scale data update	Correct incorrect fields in a small number of records, or update the status of specific fields	Correct order status, fix incorrect data in individual fields
Field-level ETL batch processing	Apply a large-scale update to a particular field, common in ETL scenarios	Batch recompute risk levels, batch refresh tag fields

:::caution Note Large-scale data updates should be infrequent operations. Avoid triggering them at high frequency. :::

How It Works

The core execution flow of UPDATE is as follows:

1. The query engine filters the rows that need to be updated based on the WHERE condition.
2. Based on the Unique Key Model's Value column overwrite logic, the new data replaces the old data.
3. The modified rows are written back to the table, achieving row-level updates.

Synchronicity

The Doris UPDATE statement is executed synchronously: as soon as the UPDATE statement returns successfully, the update operation is complete and the data is immediately visible.

Performance

The performance of an UPDATE statement is determined mainly by two factors:

Factor	Description	Optimization Suggestion
Number of rows to update	The more rows, the slower the execution	Small-scale updates can be used at the same frequency as INSERT INTO; large-scale updates are suitable only for low-frequency calls
Efficiency of the query condition	UPDATE first reads the rows that match the condition	The condition columns should hit an index or partition/bucket pruning to avoid full table scans

:::tip Tip It is strongly recommended not to include Value columns in the WHERE condition, to ensure that the query condition can efficiently leverage the primary key or indexes. :::

Usage Example

The following example uses a financial risk control scenario to demonstrate how to use UPDATE to batch-update the risk levels of transaction records.

Step 1: Create the Table

Create a transaction details table transaction_details that uses the Unique Key Model with Merge-on-Write enabled:

CREATE TABLE transaction_details (
    transaction_id BIGINT NOT NULL,        -- Unique transaction ID
    user_id BIGINT NOT NULL,               -- User ID
    transaction_date DATE NOT NULL,        -- Transaction date
    transaction_time DATETIME NOT NULL,    -- Transaction time
    transaction_amount DECIMAL(18, 2),     -- Transaction amount
    transaction_device STRING,             -- Transaction device
    transaction_region STRING,             -- Transaction region
    average_daily_amount DECIMAL(18, 2),   -- Average daily transaction amount over the last 3 months
    recent_transaction_count INT,          -- Number of transactions in the last 7 days
    has_dispute_history BOOLEAN,           -- Whether there is a dispute history
    risk_level STRING                      -- Risk level
)
UNIQUE KEY(transaction_id)
DISTRIBUTED BY HASH(transaction_id) BUCKETS 16
PROPERTIES (
    "replication_num" = "3",                       -- Number of replicas, default is 3
    "enable_unique_key_merge_on_write" = "true"    -- Enable MOW mode to support merge updates
);

Step 2: View the Initial Data

Suppose the table already contains the following transaction data, with the risk_level field not yet assigned:

+----------------+---------+------------------+---------------------+--------------------+--------------------+--------------------+----------------------+--------------------------+---------------------+------------+
| transaction_id | user_id | transaction_date | transaction_time    | transaction_amount | transaction_device | transaction_region | average_daily_amount | recent_transaction_count | has_dispute_history | risk_level |
+----------------+---------+------------------+---------------------+--------------------+--------------------+--------------------+----------------------+--------------------------+---------------------+------------+
|           1001 |    5001 | 2024-11-24       | 2024-11-24 14:30:00 |             100.00 | iPhone 12          | New York           |               100.00 |                       10 |                   0 | NULL       |
|           1002 |    5002 | 2024-11-24       | 2024-11-24 03:30:00 |             120.00 | iPhone 12          | New York           |               100.00 |                       15 |                   0 | NULL       |
|           1003 |    5003 | 2024-11-24       | 2024-11-24 10:00:00 |             150.00 | Samsung S21        | Los Angeles        |               100.00 |                       30 |                   0 | NULL       |
|           1004 |    5004 | 2024-11-24       | 2024-11-24 16:00:00 |             300.00 | MacBook Pro        | high_risk_region1  |               200.00 |                        5 |                   0 | NULL       |
|           1005 |    5005 | 2024-11-24       | 2024-11-24 11:00:00 |            1100.00 | iPad Pro           | Chicago            |               200.00 |                       10 |                   0 | NULL       |
+----------------+---------+------------------+---------------------+--------------------+--------------------+--------------------+----------------------+--------------------------+---------------------+------------+

Step 3: Define the Risk Control Rules

Apply the following priority rules to batch-tag the risk level for all transaction records of the day:

Priority	Rule	Risk Level
1	Transactions with a dispute history	high
2	Transactions from high-risk regions (such as high_risk_region1, high_risk_region2)	high
3	Transactions with abnormal amounts (more than 5 times the average daily amount)	high
4	More than 50 transactions in the last 7 days	high
5	Between 20 and 50 transactions in the last 7 days	medium
6	Transactions during off-hours (2 AM to 4 AM)	medium
7	Other default cases	low

Step 4: Execute UPDATE

Translate the rules above into a single UPDATE statement, using CASE WHEN to express the multi-condition logic:

UPDATE transaction_details
SET risk_level = CASE
    -- Transactions with a dispute history or from high-risk regions
    WHEN has_dispute_history = TRUE THEN 'high'
    WHEN transaction_region IN ('high_risk_region1', 'high_risk_region2') THEN 'high'

    -- Abnormal transaction amount
    WHEN transaction_amount > 5 * average_daily_amount THEN 'high'

    -- High-frequency transactions in the last 7 days
    WHEN recent_transaction_count > 50 THEN 'high'
    WHEN recent_transaction_count BETWEEN 20 AND 50 THEN 'medium'

    -- Transactions during off-hours
    WHEN HOUR(transaction_time) BETWEEN 2 AND 4 THEN 'medium'

    -- Default risk level
    ELSE 'low'
END
WHERE transaction_date = '2024-11-24';

Step 5: Verify the Update Results

After execution, query the data again. The risk_level field has been updated according to the rules:

+----------------+---------+------------------+---------------------+--------------------+--------------------+--------------------+----------------------+--------------------------+---------------------+------------+
| transaction_id | user_id | transaction_date | transaction_time    | transaction_amount | transaction_device | transaction_region | average_daily_amount | recent_transaction_count | has_dispute_history | risk_level |
+----------------+---------+------------------+---------------------+--------------------+--------------------+--------------------+----------------------+--------------------------+---------------------+------------+
|           1001 |    5001 | 2024-11-24       | 2024-11-24 14:30:00 |             100.00 | iPhone 12          | New York           |               100.00 |                       10 |                   0 | low        |
|           1002 |    5002 | 2024-11-24       | 2024-11-24 03:30:00 |             120.00 | iPhone 12          | New York           |               100.00 |                       15 |                   0 | medium     |
|           1003 |    5003 | 2024-11-24       | 2024-11-24 10:00:00 |             150.00 | Samsung S21        | Los Angeles        |               100.00 |                       30 |                   0 | medium     |
|           1004 |    5004 | 2024-11-24       | 2024-11-24 16:00:00 |             300.00 | MacBook Pro        | high_risk_region1  |               200.00 |                        5 |                   0 | high       |
|           1005 |    5005 | 2024-11-24       | 2024-11-24 11:00:00 |            1100.00 | iPad Pro           | Chicago            |               200.00 |                       10 |                   0 | high       |
+----------------+---------+------------------+---------------------+--------------------+--------------------+--------------------+----------------------+--------------------------+---------------------+------------+

More Help

• Full syntax reference: see the UPDATE command manual.
• Command-line help: run HELP UPDATE in the MySQL client to view the inline documentation.