Meltano

# Data Integration (EL)

Meltano lets you easily extract and load data from and to databases, SaaS APIs, and file formats using Singer taps and targets, which take the role of your project's extractors and loaders.

Meltano manages your tap and target configuration for you, makes it easy to select which entities and attributes to extract, and keeps track of the incremental replication state, so that subsequent pipeline runs with the same job ID will always pick up right where the previous run left off.

You can run EL(T) pipelines using meltano elt. If you encounter some trouble running a pipeline, read our troubleshooting tips for some errors commonly seen.

# Plugin configuration

As described in the Configuration guide, meltano elt will determine the configuration of the extractor, loader, and (optionally) transformer by looking in the environment, your project's .env file, the system database, and finally your meltano.yml project file, falling back to a default value if nothing was found.

You can use meltano config <plugin> list to list all available settings with their names, environment variables, and current values. meltano config <plugin> will print the current configuration in JSON format.

# Pipeline-specific configuration

If you'd like to specify (or override) the values of certain settings at runtime, on a per-pipeline basis, you can set them in the meltano elt execution environment using environment variables.

This lets you use the same extractors and loaders (Singer taps and targets) in multiple pipelines, configured differently each time, as an alternative to creating multiple configurations using plugin inheritance.

On a shell, you can explicitly export environment variables, that will be passed along to every following command invocation, or you can specify them in-line with a specific invocation, ahead of the command:

export TAP_FOO_BAR=bar
export TAP_FOO_BAZ=baz
meltano elt ...

TAP_FOO_BAR=bar TAP_FOO_BAZ=baz meltano elt ...

To verify that these environment variables will be picked up by Meltano as you intended, you can test them with meltano config <plugin> before running meltano elt.

If you're using meltano schedule to schedule your pipelines, you can specify environment variables for each pipeline in your meltano.yml project file, where each entry in the schedules array can have an env dictionary:







 
 
 

schedules:
- name: foo-to-bar
  extractor: tap-foo
  loader: target-bar
  transform: skip
  interval: '@hourly'
  env:
    TAP_FOO_BAR: bar
    TAP_FOO_BAZ: baz

Different runners and execution/orchestration platforms will have their own way of specifying environment variables along with a command invocation.

Airflow's BashOperator, for example, supports an env parameter:

BashOperator(
    # ...
    bash_command="meltano elt ...",
    env={
        "TAP_FOO_BAR": "bar",
        "TAP_FOO_BAZ": "baz",
    },
)

# Pipeline environment variables

To allow loaders and transformers to adapt their configuration and behavior based on the extractor and loader they are run with, meltano elt dynamically sets a number of pipeline-specific environment variables before compiling their configuration and invoking their executables.

# Extractor variables

In addition to variables available to all plugins, the following variables describing the extractor are available to loaders and transformers:

  • MELTANO_EXTRACTOR_NAME: the extractor's name, e.g. tap-gitlab
  • MELTANO_EXTRACTOR_NAMESPACE: the extractor's namespace, e.g. tap_gitlab
  • MELTANO_EXTRACT_<SETTING_NAME>: one environment variable for each of the extractor's settings and extras, e.g. MELTANO_EXTRACT_PRIVATE_TOKEN for the private_token setting, and MELTANO_EXTRACT__LOAD_SCHEMA for the load_schema extra
  • <SETTING_ENV>: all of the extractor's regular configuration environment variables, as listed by meltano config <plugin> list, e.g. TAP_GITLAB_API_URL for the api_url setting

# Loader variables

Additionally, the following variables describing the loader are available to transformers:

  • MELTANO_LOADER_NAME: the loader's name, e.g. target-postgres
  • MELTANO_LOADER_NAMESPACE: the loader's namespace, e.g. postgres
  • MELTANO_LOAD_<SETTING_NAME>: one environment variable for each of the loader's settings and extras, e.g. MELTANO_LOAD_SCHEMA for the schema setting, and MELTANO_LOAD__DIALECT for the dialect extra
  • <SETTING_ENV>: all of the loader's regular configuration environment variables, as listed by meltano config <plugin> list, e.g. PG_ADDRESS for the host setting

# Transform variables

Additionally, the following variables describing the transform are available to transformers:

  • MELTANO_TRANSFORM_NAME: the loader's name, e.g. tap-gitlab
  • MELTANO_TRANSFORM_NAMESPACE: the loader's namespace, e.g. tap_gitlab
  • MELTANO_TRANSFORM_<SETTING_NAME>: one environment variable for each of the transform's settings and extras, e.g. MELTANO_TRANSFORM__PACKAGE_NAME for the package_name extra

# How to use

Inside your loader or transformer's config object in your meltano.yml project file, you can reference these (and other) environment variables as $VAR (as a single word) or ${VAR} (inside a word). Inside your plugin, you can reference them through os.environ as usual (assuming you're using Python).

This feature is used to dynamically configure the target-postgres and target-snowflake loaders and dbt transformer as appropriate, independent of the specific extractor and loader used:

  • Default value for the target-postgres and target-snowflake schema settings:
  • Default value for dbt's target setting:
    • $MELTANO_LOAD__DIALECT, e.g. postgres for target-postgres and snowflake for target-snowflake, which correspond to the target names in transform/profile/profiles.yml
  • Default value for dbt's source_schema setting:
  • Default value for dbt's models setting:
    • $MELTANO_TRANSFORM__PACKAGE_NAME$MELTANO_EXTRACTOR_NAMESPACE my_meltano_model, e.g. tap_gitlab tap_gitlab my_meltano_model for the tap-gitlab transform and tap-gitlab extractor

# Extractor catalog generation

Many extractors (Singer taps) expect to be provided a catalog when they are run in sync mode using meltano elt or meltano invoke. This catalog is a JSON file describing the schemas of the available entities (streams, tables) and attributes (properties, columns), along with metadata to indicate (among other things) which entities and attributes should (or should not) be extracted.

A catalog can be generated by running the extractor in discovery mode and making the desired modifications to the schemas and metadata for the discovered entities and attributes. Because these catalog files can be very large and can get outdated as data sources evolve, this process can be tedious and error-prone.

To save you a headache, Meltano can handle catalog generation for you, by letting you describe your desired modifications using entity selection, metadata, and schema rules that can be configured like any other setting, and are applied to the discovered catalog on the fly when the extractor is run using meltano elt or meltano invoke.

If you'd like to manually inspect the generated catalog for debugging purposes, you can dump it to STDOUT or a file using the --dump=catalog option on meltano invoke or meltano elt.

Note that if you've already manually discovered a catalog and modified it to your liking, it can be provided explicitly using meltano elt's --catalog option or the catalog extractor extra.

# Selecting entities and attributes for extraction

Extractors are often capable of extracting many more entities and attributes than your use case may require. To save on bandwidth and storage, it's usually a good idea to instruct your extractor to only select those entities and attributes you actually plan on using.

Meltano makes it easy to select specific entities and attributes for inclusion or exclusion using meltano select and the select extractor extra, which let you specify inclusion and exclusion rules that can contain Unix shell-style wildcards to match multiple entities and/or attributes at once.

Note that exclusion takes precedence over inclusion: if an entity or attribute is matched by an exclusion pattern, there is no way to get it back using an inclusion pattern unless the exclusion pattern is manually removed from your meltano.yml project file first.

If no rules are defined using meltano select, Meltano will fall back on catch-all rule *.* so that all entities and attributes are selected.

# Setting metadata

Additional Singer stream and property metadata (like replication-method and replication-key) can be specified using the metadata extractor extra, which can be treated like a _metadata setting with nested properties _metadata.<entity>.<key> and _metadata.<entity>.<attribute>.<key>.

# Overriding schemas

Similarly, a schema extractor extra is available that lets you easily override Singer stream schema descriptions. Here too, Unix shell-style wildcards can be used to match multiple entities and/or attributes at once.

# Replication methods

Extractors can replicate data from a source using one of the following methods:

Extractors for SaaS APIs typically hard-code the appropriate replication method for each supported entity. Most database extractors, on the other hand, support two or more of these methods and require you to choose an appropriate option for each table through the replication-method stream metadata key.

To support incremental replication, where a data integration pipeline run picks up where the previous run left off, Meltano keeps track of incremental replication state.

# Log-based Incremental Replication

The extractor uses the database's binary log files to identify what records were inserted, updated, and deleted from the table since the last run (if any), and extracts only these records.

This option is not supported by all databases and database extractors.

To learn more about how Log-based Incremental Replication works and its limitations, refer to the Stitch Docs, which by and large also apply to Singer taps used with Meltano.

# Key-based Incremental Replication

The extractor uses the value of a specific column on the table (the Replication Key, e.g. an updated_at timestamp or incrementing id integer) to identify what records were inserted or updated (but not deleted) since the last run (if any), and extracts only those records.

To learn more about how Key-based Incremental Replication works and its limitations, refer to the Stitch Docs, which by and large also apply to Singer taps used with Meltano.

# Replication Key

Replication Keys are columns that database extractors use to identify new and updated data for replication.

When you set a table to use Key-based Incremental Replication, you’ll also need to define a Replication Key for that table by setting the replication-key stream metadata key.

To learn more about replication keys, refer to the Stitch Docs, which by and large also apply to Singer taps used with Meltano.

# Full Table Replication

The extractor extracts all available records in the table on every run.

To learn more about how Full-Table Replication works and its limitations, refer to the Stitch Docs, which by and large also apply to Singer taps used with Meltano.

# Incremental replication state

Most extractors (Singer taps) generate state when they are run, that can be passed along with a subsequent invocation to have the extractor pick up where it left off the previous time.

Loaders (Singer targets) take in data and state messages from extractors and are responsible for forwarding the extractor state to Meltano once the associated data has been successfully persisted in the destination.

Meltano stores this pipeline state in its system database, identified by the meltano elt run's Job ID.

When meltano elt is run a subsequent time, it will look for the most recent completed (successful or failed) pipeline run with the same job ID that generated some state. If found, this state is then passed along to the extractor.

Note that if you already have a state file you'd like to use, it can be provided explicitly using meltano elt's --state option or the state extractor extra.

If you'd like to manually inspect a pipeline's state for debugging purposes, or so that you can store it somewhere other than the system database and explicitly pass it along to the next invocation, you can dump it to STDOUT or a file using meltano elt's --dump=state option.

Not seeing state picked up after a failed run?

Some loaders only emit state once their work is completely done, even if some data may have been persisted already, and if earlier state messages from the extractor could have been forwarded to Meltano. When a pipeline with such a loader fails or is otherwise interrupted, no state will have been emitted yet, and a subsequent ELT run will not be able to pick up where this run actually left off.

# Troubleshooting

# Debug Mode

If you're running into some trouble running a pipeline, the first recommendation is to run the same command in debug mode so more information is shared on the command line.

meltano --log-level=debug elt ...

The output from debug mode will often be the first thing requested if you're asking for help via the Meltano Slack group.

# Isolate the Connector

If it's unclear which part of the pipeline is generating the problem, test the tap and target individually by using meltano invoke. The invoke command will run the executable with any specified arguments.

meltano invoke <plugin> [PLUGIN_ARGS...]

This command can also be run in debug mode for additional information.

# Validate Tap Capabilities

In cases where the tap is not loading any streams or it does not appear to be respecting the configured select rules, you may need to validate the capabilities of the tap.

In prior versions of the Singer spec, the --properties option was used instead of --catalog for the catalog files. If this is the case for a tap, ensure properties is set as a capability for the tap instead of catalog. Then meltano elt will accept the catalog file and will pass it to the tap using the appropriate flag.

# Incremental Replication Not Running as Expected

If you're trying to run a pipeline with incremental replication using meltano elt but it's running a full sync, ensure that you're passing a Job ID via the --job-id flag.

# Testing Specific Failing Streams

When extracting several streams with a single tap, it may be challenging to debug a single failing stream. In this case, it can be useful to run the tap with just the single stream selected.

Instead of duplicating the extractor in meltano.yml, try running meltano elt with the --select flag. This will run the pipeline with just that stream selected.

You can also have meltano invoke select an individual stream by setting the select_filter extra as an environment variable:

export <TAP_NAME>__SELECT_FILTER='["<your_stream>"]'

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