# Query Engine

The QuestDB Query Engine includes A custom SQL parser, a just-in-time (JIT) compiler, and a vectorized execution engine to process data in table page frames for better CPU use.

## Query engine

The QuestDB Query Engine includes A custom SQL parser, a just-in-time (JIT) compiler, and a vectorized execution engine
to process data in table page frames for better CPU use.

### SQL parsing & optimization

- **Custom SQL parser:**
  The parser supports QuestDB's SQL dialect and time-series extensions. It converts SQL queries
  into an optimized abstract syntax tree (AST).

- **Compilation pipeline:**
  The engine compiles SQL into an execution plan through stages that push down predicates and
  rewrite queries to remove unnecessary operations.

- **Optimization techniques:**
  The planner applies rule-based rewrites and simple cost estimations to choose efficient
  execution paths.

- **Columnar reads:**
   Table columns are randomly accessible. Columns with fixed size data types are read by translating
   the record number into a file offset by a simple bit shift. The offset in the column file is then
    translated into an offset in a lazily mapped memory page, where the required value is read from.

### Execution model

- **Operator pipeline:**
  The execution plan runs as a series of operators (filters, joins, aggregators) in a tightly
  integrated pipeline.

- **JIT compilation and Vectorized processing:**
  Queries with a `WHERE` clause [compile](/docs/concepts/deep-dive/jit-compiler) critical parts of the execution plan to native machine code (SIMD AVX-2 instructions) just in time. Vectorized instructions apply
  the same operation to many data elements simultaneously. This maximizes CPU cache use and reduces overhead.

- **Multi-threaded execution:**
  On top of the JIT, QuestDB tries to execute as many queries as possible in a multi-threaded,
  multi-core fashion. Some queries, for example those involving an index, are executed on a single
  thread. Other queries, like those involving `GROUP BY` and `SAMPLE BY`, execute a pipeline with some  single-threaded stages and some multi-threaded stages to avoid slow downs when groups are unbalanced.

- **Worker pools:** QuestDB allows to configure different pools for specialized functions, like
parsing incoming data, applying WAL file changes, handling PostgreSQL-Wire protocol, or responding to HTTP connections. By default, most tasks are handled by a shared worker pool.

- **Query plan caching:**
  The system caches query plans for reuse within the same connection. (Query results are not
  cached.)

- **Column data caching:**
  Data pages read from disk are kept in system memory. Sufficient memory prevents frequent disk
  reads.

## Next up

Continue to [Time-series Optimizations](/docs/architecture/time-series-optimizations/) to learn about QuestDB's time-series specific features.