InfluxQL internals reference

Learn about the implementation of InfluxQL to understand how results are processed and how to create efficient queries.

Query life cycle

  1. InfluxQL query string is tokenized and then parsed into an abstract syntax tree (AST). This is the code representation of the query itself.

  2. The AST is passed to the QueryExecutor which directs queries to the appropriate handlers. For example, queries related to meta data are executed by the meta service and SELECT statements are executed by the shards themselves.

  3. The query engine then determines the shards that match the SELECT statement’s time range. From these shards, iterators are created for each field in the statement.

  4. Iterators are passed to the emitter which drains them and joins the resulting points. The emitter’s job is to convert simple time/value points into the more complex result objects that are returned to the client.

Understanding iterators

Iterators provide a simple interface for looping over a set of points. For example, this is an iterator over Float points:

type FloatIterator interface {
    Next() *FloatPoint

These iterators are created through the IteratorCreator interface:

type IteratorCreator interface {
    CreateIterator(opt *IteratorOptions) (Iterator, error)

The IteratorOptions provide arguments about field selection, time ranges, and dimensions that the iterator creator can use when planning an iterator. The IteratorCreator interface is used at many levels such as the Shards, Shard, and Engine. This allows optimizations to be performed when applicable such as returning a precomputed COUNT().

Iterators aren’t just for reading raw data from storage though. Iterators can be composed so that they provided additional functionality around an input iterator. For example, a DistinctIterator can compute the distinct values for each time window for an input iterator. Or a FillIterator can generate additional points that are missing from an input iterator.

This composition also lends itself well to aggregation. For example, a statement such as this:

SELECT MEAN(value) FROM cpu GROUP BY time(10m)

In this case, MEAN(value) is a MeanIterator wrapping an iterator from the underlying shards. However, if we can add an additional iterator to determine the derivative of the mean:

SELECT DERIVATIVE(MEAN(value), 20m) FROM cpu GROUP BY time(10m)


A cursor identifies data by shard in tuples (time, value) for a single series (measurement, tag set and field). The cursor trasverses data stored as a log-structured merge-tree and handles deduplication across levels, tombstones for deleted data, and merging the cache (Write Ahead Log). A cursor sorts the (time, value) tuples by time in ascending or descending order.

For example, a query that evaluates one field for 1,000 series over 3 shards constructs a minimum of 3,000 cursors (1,000 per shard).

Auxiliary fields

Because InfluxQL allows users to use selector functions such as FIRST(), LAST(), MIN(), and MAX(), the engine must provide a way to return related data at the same time with the selected point.

For example, in this query:

SELECT FIRST(value), host FROM cpu GROUP BY time(1h)

We are selecting the first value that occurs every hour but we also want to retrieve the host associated with that point. Since the Point types only specify a single typed Value for efficiency, we push the host into the auxiliary fields of the point. These auxiliary fields are attached to the point until it is passed to the emitter where the fields get split off to their own iterator.

Built-in iterators

There are many helper iterators that let us build queries:

  • Merge Iterator - This iterator combines one or more iterators into a single new iterator of the same type. This iterator guarantees that all points within a window will be output before starting the next window but does not provide ordering guarantees within the window. This allows for fast access for aggregate queries which do not need stronger sorting guarantees.

  • Sorted Merge Iterator - This iterator also combines one or more iterators into a new iterator of the same type. However, this iterator guarantees time ordering of every point. This makes it slower than the MergeIterator but this ordering guarantee is required for non-aggregate queries which return the raw data points.

  • Limit Iterator - This iterator limits the number of points per name/tag group. This is the implementation of the LIMIT & OFFSET syntax.

  • Fill Iterator - This iterator injects extra points if they are missing from the input iterator. It can provide null points, points with the previous value, or points with a specific value.

  • Buffered Iterator - This iterator provides the ability to “unread” a point back onto a buffer so it can be read again next time. This is used extensively to provide lookahead for windowing.

  • Reduce Iterator - This iterator calls a reduction function for each point in a window. When the window is complete then all points for that window are output. This is used for simple aggregate functions such as COUNT().

  • Reduce Slice Iterator - This iterator collects all points for a window first and then passes them all to a reduction function at once. The results are returned from the iterator. This is used for aggregate functions such as DERIVATIVE().

  • Transform Iterator - This iterator calls a transform function for each point from an input iterator. This is used for executing binary expressions.

  • Dedupe Iterator - This iterator only outputs unique points. It is resource intensive so it is only used for small queries such as meta query statements.

Call iterators

Function calls in InfluxQL are implemented at two levels. Some calls can be wrapped at multiple layers to improve efficiency. For example, a COUNT() can be performed at the shard level and then multiple CountIterators can be wrapped with another CountIterator to compute the count of all shards. These iterators can be created using NewCallIterator().

Some iterators are more complex or need to be implemented at a higher level. For example, the DERIVATIVE() needs to retrieve all points for a window first before performing the calculation. This iterator is created by the engine itself and is never requested to be created by the lower levels.

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