8.2.1.10 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:

• MRR enables data rows to be accessed sequentially rather than in random order, based on index tuples. The server obtains a set of index tuples that satisfy the query conditions, sorts them according to data row ID order, and uses the sorted tuples to retrieve data rows in order. This makes data access more efficient and less expensive.

• MRR enables batch processing of requests for key access for operations that require access to data rows through index tuples, such as range index scans and equi-joins that use an index for the join attribute. MRR iterates over a sequence of index ranges to obtain qualifying index tuples. As these results accumulate, they are used to access the corresponding data rows. It is not necessary to acquire all index tuples before starting to read data rows.

The MRR optimization is not supported with secondary indexes created on virtual generated columns. InnoDB supports secondary indexes on virtual generated columns.

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.

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 8.9.2, “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.