Distributed SQL

By now we already know how TiDB’s relational structure is encoded into the Key-Value form with version. In this section, we will focus on the following questions:

  • What happens when TiDB receives a SQL query?
  • How does TiDB execute SQL queries in a distributed way?

What happens when TiDB receives a SQL query?

Firstly, let’s have a look at the following example:

select count(*) from t where a + b  > 5;
Figure 1. SQL query diagram

As described in the above figure, when TiDB receives a SQL query from the client, it will process with the following steps:

  1. TiDB receives a new SQL from the client.
  2. TiDB prepares the processing plans for this request, meanwhile TiDB gets a timestamp from PD as the start_ts of this transaction.
  3. TiDB tries to get the information schema (metadata of the table) from TiKV.
  4. TiDB prepares Regions for each related key according to the information schema and the SQL query. Then TiDB gets information for the related Regions from PD.
  5. TiDB groups the related keys by Region.
  6. TiDB dispatches the tasks to the related TiKV concurrently.
  7. TiDB reassembles the data and returns the data to the client.

How does TiDB execute SQL queries in a distributed way?

In short, TiDB splits the task by Regions and sends them to TiKV concurrently.

For the above example, we assume the rows with the primary key of table t are distributed in three Regions:

  • Rows with the primary key in [0,100) are in Region 1.
  • Rows with primary key in [100,1000) are in region 2.
  • Rows with primary key in [1000,~) are in region 3.

Now we can do count and sum the result from the above three Regions.

Figure 2. Coprocessor diagram

Exectors

Now we know TiDB splits a read task by Regions, but how does TiKV know what are its tasks to handle? Here TiDB will send a Directed Acyclic Graph (DAG) to TiKV with each node as an executor.

Figure 3. Executors

Supported executors:

  • TableScan: Scans the rows with the primary key from the KV store.
  • IndexScan: It will scan the index data from the KV store.
  • Selection: performs a filter (mostly for where). The input is TableScan or IndexScan.
  • Aggregation: performs an aggregation (e.g. count(*),sum(xxx)). The input is TableScan,IndexScan, orSelection.
  • TopN: sorts the data and returns the top n matches, for example, order by xxx limit 10. The input is TableScan,IndexScan, orSelection.
Figure 4. Executors example

executors-example

For the above example, we have the following executors on Region 1:

  • Aggregation: count(*).
  • Selection: a + b > 5
  • TableScan: range:[0,100).

Expression

We have executors as nodes in the DAG, but how do we describe columns, constants, and functions in an Aggregation or a Selection? There are three types of expressions:

  • Column: a column in the table.
  • Constant: a constant, which could be a string, int, duration, and so on.
  • Scalar function: describes a function.
Figure 5. Expression

For the above example select count(*) from t where a + b > 5, we have:

  • Column: a, b.
  • Scalar functions: +,>.
  • Constant: 5.