Fast Updates with TokuDB


Update intensive applications can have their throughput limited by the random read capacity of the storage system. The cause of the throughput limit is the read-modify-write algorithm that MySQL uses to process update statements (read a row from the storage engine, apply the updates to it, write the new row back to the storage engine).

To address this throughput limit, TokuDB provides an experimental fast update feature, which uses a different update algorithm. Update expressions of the SQL statement are encoded into tiny programs that are stored in an update Fractal Tree message. This update message is injected into the root of the Fractal Tree index. Eventually, these update messages reach a leaf node, where the update programs are applied to the row. Since messages are moved between Fractal Tree levels in batches, the cost of reading in the leaf node is amortized over many update messages.

This feature is available for UPDATE and INSERT statements, and can be turned ON/OFF separately for them with use of two variables. Variable tokudb_enable_fast_update variable toggles fast updates for the UPDATE, and tokudb_enable_fast_upsert does the same for INSERT.


Fast updates are activated instead of normal MySQL read-modify-write updates if the executed expression meets the number of conditions.

  • fast updates can be activated for a statement or a mixed replication,
  • a primary key must be defined for the involved table,
  • both simple and compound primary keys are supported, and int, char or varchar are the allowed data types for them,
  • updated fields should have Integer or char data type,
  • fields that are part of any key should be not updated,
  • clustering keys are not allowed,
  • triggers should be not involved,
  • supported update expressions should belong to one of the following types:
    • x = constant
    • x = x + constant
    • x = x - constant
    • x = if (x=0,0,x-1)
    • x = x + values

Usage Specifics and Examples

Following example creates a table that associates event identifiers with their count:

    event_id bigint unsigned NOT NULL PRIMARY KEY,
    event_count bigint unsigned NOT NULL

Many graph applications that map onto relational tables can use duplicate key inserts and updates to maintain the graph. For example, one can update the meta-data associated with a link in the graph using duplicate key insertions. If the affected rows is not used by the application, then the insertion or update can be marked and executed as a fast insertion or a fast update.

Insertion example

If it is not known if the event identifier (represented by event_id) already exists in the table, then INSERT ... ON DUPLICATE KEY UPDATE ... statement can insert it if not existing, or increment its event_count otherwise. Here is an example with duplicate key insertion statement, where %id is some specific event_id value:

  ON DUPLICATE KEY UPDATE event_count=event_count+1;


If the event id’s are random, then the throughput of this application would be limited by the random read capacity of the storage system since each INSERT statement has to determine if this event_id exists in the table.

TokuDB replaces the primary key existence check with an insertion of an “upsert” message into the Fractal Tree index. This “upsert” message contains a copy of the row and a program that increments event_count. As the Fractal Tree buffer’s get filled, this “upsert” message is flushed down the tree. Eventually, the message reaches a leaf node and gets executed there. If the key exists in the leaf node, then the event_count is incremented. Otherwise, the new row is inserted into the leaf node.

Update example

If event_id is known to exist in the table, then UPDATE statement can be used to increment its event_count (once again, specific event_id value is written here as %id):

UPDATE t SET event_count=event_count+1
WHERE event_id=%id;


TokuDB generates an “update” message from the UPDATE statement and its update expression trees, and inserts this message into the Fractal Tree index. When the message eventually reaches the leaf node, the increment program is extracted from the message and executed.

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