8.2.1.13 Multi-Range Read Optimization

Reading rows using a range scan on a secondary index can result in many random disk accesses to the base table when the table is large and not stored in the storage engine's cache. With the Disk-Sweep Multi-Range Read (MRR) optimization, MySQL tries to reduce the number of random disk access for range scans by first scanning the index only and collecting the keys for the relevant rows. Then the keys are sorted and finally the rows are retrieved from the base table using the order of the primary key. The motivation for Disk-sweep MRR is to reduce the number of random disk accesses and instead achieve a more sequential scan of the base table data.

The Multi-Range Read optimization provides these benefits:

The following scenarios illustrate when MRR optimization can be advantageous:

Scenario A: MRR can be used for InnoDB and MyISAM tables for index range scans and equi-join operations.

  1. A portion of the index tuples are accumulated in a buffer.

  2. The tuples in the buffer are sorted by their data row ID.

  3. Data rows are accessed according to the sorted index tuple sequence.

Scenario B: MRR can be used for NDB tables for multiple-range index scans or when performing an equi-join by an attribute.

  1. A portion of ranges, possibly single-key ranges, is accumulated in a buffer on the central node where the query is submitted.

  2. The ranges are sent to the execution nodes that access data rows.

  3. The accessed rows are packed into packages and sent back to the central node.

  4. The received packages with data rows are placed in a buffer.

  5. Data rows are read from the buffer.

When MRR is used, the Extra column in EXPLAIN output shows Using MRR.

InnoDB and MyISAM do not use MRR if full table rows need not be accessed to produce the query result. This is the case if results can be produced entirely on the basis on information in the index tuples (through a covering index); MRR provides no benefit.

Example query for which MRR can be used, assuming that there is an index on (key_part1, key_part2):

SELECT * FROM t
  WHERE key_part1 >= 1000 AND key_part1 < 2000
  AND key_part2 = 10000;

The index consists of tuples of (key_part1, key_part2) values, ordered first by key_part1 and then by key_part2.

Without MRR, an index scan covers all index tuples for the key_part1 range from 1000 up to 2000, regardless of the key_part2 value in these tuples. The scan does extra work to the extent that tuples in the range contain key_part2 values other than 10000.

With MRR, the scan is broken up into multiple ranges, each for a single value of key_part1 (1000, 1001, ... , 1999). Each of these scans need look only for tuples with key_part2 = 10000. If the index contains many tuples for which key_part2 is not 10000, MRR results in many fewer index tuples being read.

To express this using interval notation, the non-MRR scan must examine the index range [{1000, 10000}, {2000, MIN_INT}), which may include many tuples other than those for which key_part2 = 10000. The MRR scan examines multiple single-point intervals [{1000, 10000}], ..., [{1999, 10000}], which includes only tuples with key_part2 = 10000.

Two optimizer_switch system variable flags provide an interface to the use of MRR optimization. The mrr flag controls whether MRR is enabled. If mrr is enabled (on), the mrr_cost_based flag controls whether the optimizer attempts to make a cost-based choice between using and not using MRR (on) or uses MRR whenever possible (off). By default, mrr is on and mrr_cost_based is on. See Section 8.8.5.2, “Controlling Switchable Optimizations”.

For MRR, a storage engine uses the value of the read_rnd_buffer_size system variable as a guideline for how much memory it can allocate for its buffer. The engine uses up to read_rnd_buffer_size bytes and determines the number of ranges to process in a single pass.