Connectors are the source of all data for queries in Trino. Even if your data source doesn’t have underlying tables backing it, as long as you adapt your data source to the API expected by Trino, you can write queries against this data.


Instances of your connector are created by a ConnectorFactory instance which is created when Trino calls getConnectorFactory() on the plugin. The connector factory is a simple interface responsible for providing the connector name and creating an instance of a Connector object. A basic connector implementation that only supports reading, but not writing data, should return instances of the following services:


The create() method of the connector factory receives a config map, containing all properties from the catalog properties file. It can be used to configure the connector, but because all the values are strings, they might require additional processing if they represent other data types. It also doesn’t validate if all the provided properties are known. This can lead to the connector behaving differently than expected when a connector ignores a property due to the user making a mistake in typing the name of the property.

To make the configuration more robust, define a Configuration class. This class describes all the available properties, their types, and additional validation rules.

import io.airlift.configuration.Config;
import io.airlift.configuration.ConfigDescription;
import io.airlift.configuration.ConfigSecuritySensitive;
import io.airlift.units.Duration;
import io.airlift.units.MaxDuration;
import io.airlift.units.MinDuration;

import javax.validation.constraints.NotNull;

public class ExampleConfig
    private String secret;
    private Duration timeout = Duration.succinctDuration(10, TimeUnit.SECONDS);

    public String getSecret()
        return secret;

    @ConfigDescription("Secret required to access the data source")
    public ExampleConfig setSecret(String secret)
        this.secret = secret;
        return this;

    public Duration getTimeout()
        return timeout;

    public ExampleConfig setTimeout(Duration timeout)
        this.timeout = timeout;
        return this;

The preceding example defines two configuration properties and makes the connector more robust by:

  • defining all supported properties, which allows detecting spelling mistakes in the configuration on server startup

  • defining a default timeout value, to prevent connections getting stuck indefinitely

  • preventing invalid timeout values, like 0 ms, that would make all requests fail

  • parsing timeout values in different units, detecting invalid values

  • preventing logging the secret value in plain text

The configuration class needs to be bound in a Guice module:


import static io.airlift.configuration.ConfigBinder.configBinder;

public class ExampleModule
        implements Module
    public ExampleModule()

    public void configure(Binder binder)

And then the module needs to be initialized in the connector factory, when creating a new instance of the connector:

public Connector create(String connectorName, Map<String, String> config, ConnectorContext context)
    requireNonNull(config, "config is null");
    Bootstrap app = new Bootstrap(new ExampleModule());
    Injector injector = app

    return injector.getInstance(ExampleConnector.class);


Environment variables in the catalog properties file (ex. secret=${ENV:SECRET}) are resolved only when using the io.airlift.bootstrap.Bootstrap class to initialize the module. See Secrets for more information.

If you end up needing to define multiple catalogs using the same connector just to change one property, consider adding support for schema and/or table properties. That would allow a more fine-grained configuration. If a connector doesn’t support managing the schema, query predicates for selected columns could be used as a way of passing the required configuration at run time.

For example, when building a connector to read commits from a Git repository, the repository URL could be a configuration property. But this would result in a catalog being able to return data only from a single repository. Alternatively, it can be a column, where every select query would require a predicate for it:

FROM git.default.commits
WHERE url = ''


The connector metadata interface allows Trino to get a lists of schemas, tables, columns, and other metadata about a particular data source.

A basic read-only connector should implement the following methods:

  • listSchemaNames

  • listTables

  • streamTableColumns

  • getTableHandle

  • getTableMetadata

  • getColumnHandles

  • getColumnMetadata

If you are interested in seeing strategies for implementing more methods, look at the Example HTTP connector and the Cassandra connector. If your underlying data source supports schemas, tables, and columns, this interface should be straightforward to implement. If you are attempting to adapt something that isn’t a relational database, as the Example HTTP connector does, you may need to get creative about how you map your data source to Trino’s schema, table, and column concepts.

The connector metadata interface allows to also implement other connector features, like:

  • Schema management, which is creating, altering and dropping schemas, tables, table columns, views, and materialized views.

  • Support for table and column comments, and properties.

  • Schema, table and view authorization.

  • Executing Table functions.

  • Providing table statistics used by the Cost Based Optimizer (CBO) and collecting statistics during writes and when analyzing selected tables.

  • Data modification, which is:

    • inserting, updating, and deleting rows in tables,

    • refreshing materialized views,

    • truncating whole tables,

    • and creating tables from query results.

  • Role and grant management.

  • Pushing down:

Note that data modification also requires implementing a ConnectorPageSinkProvider.

When Trino receives a SELECT query, it parses it into an Intermediate Representation (IR). Then, during optimization, it checks if connectors can handle operations related to SQL clauses by calling one of the following methods of the ConnectorMetadata service:

  • applyLimit

  • applyTopN

  • applyFilter

  • applyProjection

  • applySample

  • applyAggregation

  • applyJoin

  • applyTableFunction

  • applyTableScanRedirect

Connectors can indicate that they don’t support a particular pushdown or that the action had no effect by returning Optional.empty(). Connectors should expect these methods to be called multiple times during the optimization of a given query.


It’s critical for connectors to return Optional.empty() if calling this method has no effect for that invocation, even if the connector generally supports a particular pushdown. Doing otherwise can cause the optimizer to loop indefinitely.

Otherwise, these methods return a result object containing a new table handle.

The new table handle represents the virtual table derived from applying the operation (filter, project, limit, etc.) to the table produced by the table scan node.

The returned table handle is later passed to other services that the connector implements, like the ConnectorRecordSetProvider or ConnectorPageSourceProvider.

Limit and top-N pushdown#

When executing a SELECT query with LIMIT or ORDER BY clauses, the query plan may contain a Sort or Limit operations.

When the plan contains a Sort and Limit operations, the engine tries to push down the limit into the connector by calling the applyTopN method of the connector metadata service. If there’s no Sort operation, but only a Limit, the applyLimit method is called, and the connector can return results in an arbitrary order.

If the connector could benefit from the information passed to these methods but can’t guarantee that it’s be able to produce fewer rows than the provided limit, it should return a non-empty result containing a new handle for the derived table and the limitGuaranteed (in LimitApplicationResult) or topNGuaranteed (in TopNApplicationResult) flag set to false.

If the connector can guarantee to produce fewer rows than the provided limit, it should return a non-empty result with the “limit guaranteed” or “topN guaranteed” flag set to true.


The applyTopN is the only method that receives sort items from the Sort operation.

In an SQL query, the ORDER BY section can include any column with any order. But the data source for the connector might only support limited combinations. Plugin authors have to decide if the connector should ignore the pushdown, return all the data and let the engine sort it, or throw an exception to inform the user that particular order isn’t supported, if fetching all the data would be too expensive or time consuming. When throwing an exception, use the TrinoException class with the INVALID_ORDER_BY error code and an actionable message, to let users know how to write a valid query.


The split manager partitions the data for a table into the individual chunks that Trino distributes to workers for processing. For example, the Hive connector lists the files for each Hive partition and creates one or more splits per file. For data sources that don’t have partitioned data, a good strategy here is to simply return a single split for the entire table. This is the strategy employed by the Example HTTP connector.


Given a split, a table handle, and a list of columns, the record set provider is responsible for delivering data to the Trino execution engine.

The table and column handles represent a virtual table. They’re created by the connector’s metadata service, called by Trino during query planning and optimization. Such a virtual table doesn’t have to map directly to a single collection in the connector’s data source. If the connector supports pushdowns, there can be multiple virtual tables derived from others, presenting a different view of the underlying data.

The provider creates a RecordSet, which in turn creates a RecordCursor that’s used by Trino to read the column values for each row.

The provided record set must only include requested columns in the order matching the list of column handles passed to the ConnectorRecordSetProvider.getRecordSet() method. The record set must return all the rows contained in the “virtual table” represented by the TableHandle associated with the TableScan operation.

For simple connectors, where performance isn’t critical, the record set provider can return an instance of InMemoryRecordSet. The in-memory record set can be built using lists of values for every row, which can be simpler than implementing a RecordCursor.

A RecordCursor implementation needs to keep track of the current record. It return values for columns by a numerical position, in the data type matching the column definition in the table. When the engine is done reading the current record it calls advanceNextPosition on the cursor.

Type mapping#

The built-in SQL data types use different Java types as carrier types.

SQL type to carrier type mapping#

SQL type

Java type
















long for precision up to 19, inclusive; Int128 for precision greater than 19














long for precision up to 9; LongTimeWithTimeZone for precision greater than 9


long for precision up to 6; LongTimestamp for precision greater than 6


long for precision up to 3; LongTimestampWithTimeZone for precision greater than 3

























The RecordCursor.getType(int field) method returns the SQL type for a field and the field value is returned by one of the following methods, matching the carrier type:

  • getBoolean(int field)

  • getLong(int field)

  • getDouble(int field)

  • getSlice(int field)

  • getObject(int field)

Values for the timestamp(p) with time zone and time(p) with time zone types of regular precision can be converted into long using static methods from the io.trino.spi.type.DateTimeEncoding class, like pack() or packDateTimeWithZone().

UTF-8 encoded strings can be converted to Slices using the Slices.utf8Slice() static method.


The Slice class is provided by the io.airlift:slice package.

Int128 objects can be created using the Int128.valueOf() method.

The following example creates a block for an array(varchar) column:

private Block encodeArray(List<String> names)
    BlockBuilder builder = VARCHAR.createBlockBuilder(null, names.size());
    for (String name : names) {
        if (name == null) {
        else {
            VARCHAR.writeString(builder, name);

The following example creates a block for a map(varchar, varchar) column:

private Block encodeMap(Map<String, ?> map)
    MapType mapType = typeManager.getType(TypeSignature.mapType(
    BlockBuilder values = mapType.createBlockBuilder(null, map != null ? map.size() : 0);
    if (map == null) {
        return, Block.class);
    BlockBuilder builder = values.beginBlockEntry();
    for (Map.Entry<String, ?> entry : map.entrySet()) {
        VARCHAR.writeString(builder, entry.getKey());
        Object value = entry.getValue();
        if (value == null) {
        else {
            VARCHAR.writeString(builder, value.toString());
    return, Block.class);


Given a split, a table handle, and a list of columns, the page source provider is responsible for delivering data to the Trino execution engine. It creates a ConnectorPageSource, which in turn creates Page objects that are used by Trino to read the column values.

If not implemented, a default RecordPageSourceProvider is used. Given a record set provider, it returns an instance of RecordPageSource that builds Page objects from records in a record set.

A connector should implement a page source provider instead of a record set provider when it’s possible to create pages directly. The conversion of individual records from a record set provider into pages adds overheads during query execution.

To add support for updating and/or deleting rows in a connector, it needs to implement a ConnectorPageSourceProvider that returns an UpdatablePageSource. See Supporting DELETE and UPDATE for more.


Given an insert table handle, the page sink provider is responsible for consuming data from the Trino execution engine. It creates a ConnectorPageSink, which in turn accepts Page objects that contains the column values.

Example that shows how to iterate over the page to access single values:

public CompletableFuture<?> appendPage(Page page)
    for (int channel = 0; channel < page.getChannelCount(); channel++) {
        Block block = page.getBlock(channel);
        for (int position = 0; position < page.getPositionCount(); position++) {
            if (block.isNull(position)) {
                // or handle this differently

            // channel should match the column number in the table
            // use it to determine the expected column type
            String value = VARCHAR.getSlice(block, position).toStringUtf8();
            // TODO do something with the value
    return NOT_BLOCKED;