Building with Matchbox DAGs

Data matching and entity resolution are complex tasks that often require multiple processing steps. Matchbox provides a powerful Directed Acyclic Graph (DAG) framework that allows you to define and run sophisticated matching pipelines with clearly defined dependencies.

DAG steps and resolutions

Once it is run, a step in a DAG corresponds to a resolution in the Matchbox database. Learn more by reading about the tutorial on exploring resolutions or dive deeper by consulting the extended guide to Matchbox concepts.

This guide walks through creating complete matching pipelines using the Matchbox DAG API, covering everything from defining data sources to executing complex multi-step matching processes. In our examples we’ll be referencing publicly available datasets about UK companies, specifically Companies House data, and UK trade data.

Understanding DAGs in Matchbox

A DAG (Directed Acyclic Graph) represents a sequence of operations where each step depends on the outputs of previous steps, without any circular dependencies. In Matchbox, a DAG consists of:

1. IndexSteps: Loading and indexing data from sources
2. DedupeSteps: Removing duplicates within a source
3. LinkSteps: Connecting records between different sources

Setting up your environment

Before building a pipeline, ensure you have Matchbox properly installed and configured:

import logging
from matchbox.client import clean
from matchbox.client.dags import DAG, DedupeStep, IndexStep, LinkStep, StepInput
from matchbox.client.models.dedupers.naive import NaiveDeduper
from matchbox.client.models.linkers import DeterministicLinker
from matchbox.common.sources import SourceConfig, RelationalDBLocation

# Configure logging
logging.basicConfig(
    level=logging.DEBUG,
    format="%(asctime)s [%(name)s] %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)
logger = logging.getLogger()

# Reduce noise from HTTP libraries
logging.getLogger("httpcore").setLevel(logging.WARNING)
logging.getLogger("httpx").setLevel(logging.WARNING)
logging.getLogger("http").setLevel(logging.WARNING)

# Get your database engine
from your_utils import get_database_engine
engine = get_database_engine()

1. Defining data sources

The first step in creating a matching pipeline is to define your data sources. Each source represents data that will be used in the matching process.

The index_fields are what Matchbox will use to store a reference to your data, and are the only fields it will permit you to match on.

The key_field is the field in your source that contains some unique code that identifies each entitiy. For example, in a relational database, this would typically be your primary key.

Example

from matchbox.common.sources import SourceConfig, RelationalDBLocation

# Companies House data
companies_house = SourceConfig.new(
    name="companies_house",
    location=RelationalDBLocation(name="dbname", client=engine),
    extract_transform="""
        select
            pk as id,
            company_name,
            number::str as company_number,
            upper(postcode) as postcode,
        from
            companieshouse.companies;
    """,
    index_fields=["company_name", "company_number", "postcode"],
    key_field="id",
)

# Exporters data
exporters = SourceConfig.new(
    name="hmrc_exporters",
    location=RelationalDBLocation(name="dbname", client=engine),
    extract_transform="""
        select
            id,
            company_name,
            upper(postcode) as postcode,
        from
            hmrc.trade__exporters;
    """,
    index_fields=["company_name", "postcode"],
    key_field="id",
)

Each SourceConfig object requires:

• A location, such as RelationalDBLocation. This will need a type, a name, and client, the type of which changes depending on the type of location you’re using
  • The name of a location is a way of tagging it, such that later on you can filter source configs you want to retrieve from the server
  • For a relational database, a SQLAlchemy engine is your client
• An extract_transform string, which will take data from the location and transform it into your key and index fields. Its syntax will depend on the type of location
  • For most users, using a relational database location, this will be SQL
• A list of index_fields that will be used for matching
  • These must be found in the result of the extract_transform logic
• A key field (key_field) that uniquely identifies each record
  • This must be found in the result of the extract_transform logic

If a SourceConfig has already been created you can fetch it, with optional validation, from the server.

Example

from matchbox import get_source

# Companies House data
companies_house = get_source(
    name="companies_house",
    location=RelationalDBLocation(name="dbname", client=engine),
    extract_transform="""
        select
            pk as id,
            company_name,
            number::str as company_number,
            upper(postcode) as postcode,
        from
            companieshouse.companies;
    """,
    index_fields=["company_name", "company_number", "postcode"],
    key_field="id",
)

# Exporters data
exporters = get_source(name="hmrc_exporters")

2. Creating index steps

Index steps load data from your sources into Matchbox. Matchbox never sees your data, storing only a reference to it.

Only data indexed in Matchbox can we used to match.

Example

from matchbox.client.dags import IndexStep

# Define batch size
batch_size = 250_000

# Create index steps
i_companies = IndexStep(source_config=companies_house, batch_size=batch_size)
i_exporters = IndexStep(source_config=exporters, batch_size=batch_size)

Each IndexStep requires:

• A source object
• An optional batch_size for processing large data in chunks

3. Creating dedupe steps

Dedupe steps identify and resolve duplicates within a single source.

Example

from matchbox.client.dags import DedupeStep, StepInput
from matchbox.client.models.dedupers.naive import NaiveDeduper

# Deduplicate Companies House data based on company number
dedupe_companies = DedupeStep(
    left=StepInput(
        prev_node=i_companies,
        select={companies_house: ["company_name", "company_number"]},
        cleaning_dict={
            "company_name": f"lower({companies_house.f('company_name')})",
        },
        batch_size=batch_size,
    ),
    name="naive_companieshouse_companies",
    description="Deduplication based on company number",
    model_class=NaiveDeduper,
    settings={
        "id": "id",
        "unique_fields": [
            "company_name",
            companies_house.f("company_number"),
        ],
    },
    truth=1.0,
)

A DedupeStep requires:

• A left input, defined as a StepInput that specifies:
  • The previous step (prev_node)
  • Which fields to select (select)
  • Cleaning operations to apply (cleaning_dict)
  • Optional batch size
• A unique name for the step
• A description explaining the purpose of the step
• The deduplication algorithm to use (model_class)
• Configuration settings for the algorithm
• A truth threshold (a float between 0.0 and 1.0) above which a match is considered “true”

On cleaning

Simplify field references by cleaning everything

To avoid confusion with qualified vs unqualified field names, consider “cleaning” every field you select - even if you’re just aliasing it without transformation. This way, all your field references use simple, unqualified names throughout your configuration.

# Instead of mixing qualified and unqualified names
cleaning_dict={
    "company_name": f"lower({companies_house.f('company_name')})",
    # company_number not cleaned, so needs qualification later
}
settings={
    "unique_fields": [
        "company_name",
        companies_house.f("company_number"),  # Qualified!
    ],
}

# Clean everything for consistency
cleaning_dict={
    "company_name": f"lower({companies_house.f('company_name')})",
    "company_number": companies_house.f("company_number"),  # Just aliasing
}
settings={
    "unique_fields": [
        "company_name",
        "company_number",  # Both unqualified!
    ],
}

This approach makes your configuration much more readable and reduces errors from forgetting to qualify field names.

It’s worth understanding how data moves through Step objects, as it helps knowing when or if to qualify column names. When would I use "company_number" vs companies_house.f("company_number"), for example?

StepInput.select will be used to extract data to a columnar format. More complex select objects will often select the same column names from multiple sources, so column names must be qualified with their source.

Here’s the select statement from the above example:

select={companies_house: ["company_name", "company_number"]}

And the raw data it outputs might look like this:

id	companieshouse_companies_company_name	companieshouse_companies_company_number
1	Acme Corporation Ltd	12345678
2	ACME CORPORATION LTD	12345678
3	Beta Solutions Inc	87654321
4	Gamma Technologies PLC	11223344
5	GAMMA TECHNOLOGIES plc	11223344

Note how the fields specified in select are “qualified” with the source they came from.

Next, StepInput.cleaning_dict will be applied. Each of its keys will be an output column defined by the SQL in its value. SourceConfig.f() is provided as a convenient way to select fields qualified by a source.

The rules for the cleaning dictionary are:

• The ID column is automatically passed through
• If a column is mentioned in any cleaning SQL, its uncleaned version is automatically dropped from the output
• If a column isn’t mentioned in any cleaning SQL, it’s automatically passed through with its qualified name

Here’s the cleaning dictionary from the above example:

cleaning_dict={
    "company_name": f"lower({companies_house.f('company_name')})",
}

Note how we qualify the field we clean and alias it to company_name, and that company_number isn’t mentioned.

The cleaned data it outputs might look like this:

id	company_name	companieshouse_companies_company_number
1	acme corporation ltd	12345678
2	acme corporation ltd	12345678
3	beta solutions inc	87654321
4	gamma technologies plc	11223344
5	gamma technologies plc	11223344

Finally, cleaned fields typically need specifying in a model. Here’s our example:

settings={
    "id": "id",
    "unique_fields": [
        "company_name",
        companies_house.f("company_number"),
    ],
}

Note that because we didn’t clean company_number it needs to be qualified here, rather than in the cleaning dictionary.

4. Creating link steps

Link steps connect records between different sources.

Example

from matchbox.client.dags import LinkStep
from matchbox.client.models.linkers import DeterministicLinker

# Link exporters and importers based on name and postcode
link_exp_imp = LinkStep(
    left=StepInput(
        prev_node=dedupe_exporters,
        select={exporters: ["company_name", "postcode"]},
        cleaning_dict={
            "company_name": f"lower({exporters.f('company_name')})",
            "postcode": exporters.f("postcode"),
        },
        batch_size=batch_size,
    ),
    right=StepInput(
        prev_node=dedupe_importers,
        select={importers: ["company_name", "postcode"]},
        cleaning_dict={
            "company_name": f"lower({importers.f('company_name')})",
            "postcode": importers.f("postcode"),
        },
        batch_size=batch_size,
    ),
    name="deterministic_exp_imp",
    description="Deterministic link on names and postcode",
    model_class=DeterministicLinker,
    settings={
        "left_id": "id",
        "right_id": "id",
        "comparisons": [
            """
                l.company_name = r.company_name
                    and l.postcode= r.postcode
            """
        ],
    },
    truth=1.0,
)

A LinkStep requires:

• A left and right input, defined as a StepInput that specifies:
  • The previous step (prev_node)
  • Which fields to select (select)
  • Cleaning operations to apply (cleaning_dict)
  • Optional batch size
• A unique name for the step
• A description explaining the purpose of the step
• The linking algorithm to use (model_class)
• Configuration settings for the algorithm
• A truth threshold (a float between 0.0 and 1.0) above which a match is considered “true”

As with deduplication, the cleaning_dict maps field aliases to DuckDB SQL expressions that can reference input columns. See On cleaning for how to specify this functionality, or check the documentation for (clean())

Available linker types

Matchbox provides several linking methodologies:

1. DeterministicLinker: Links records based on exact matches of specified fields

   from matchbox.client.models.linkers import DeterministicLinker
   
   settings = {
       "left_id": "id",
       "right_id": "id",
       "comparisons": "l.name = r.name and l.postcode = r.postcode"
   }
   

2. WeightedDeterministicLinker: Assigns different weights to different comparison fields

   from matchbox.client.models.linkers import WeightedDeterministicLinker
   
   settings = {
       "left_id": "id",
       "right_id": "id",
       "weighted_comparisons": [
           {"comparison": "l.company_name = r.company_name", "weight": 0.7},
           {"comparison": "l.postcode = r.postcode", "weight": 0.3}
       ],
       "threshold": 0.8
   }
   

3. SplinkLinker: Uses probabilistic matching with the Splink library

   from matchbox.client.models.linkers import SplinkLinker
   from splink import SettingsCreator
   import splink.comparison_library as cl
   
   splink_settings = SettingsCreator(
       link_type="link_only",
       blocking_rules_to_generate_predictions=["l.postcode = r.postcode"],
       comparisons=[
           cl.jaro_winkler_at_thresholds("company_name", [0.9, 0.6], term_frequency_adjustments=True)
       ]
   )
   
   settings = {
       "left_id": "id",
       "right_id": "id",
       "linker_settings": splink_settings,
       "linker_training_functions": [
           {
               "function": "estimate_probability_two_random_records_match",
               "arguments": {
                   "deterministic_matching_rules": "l.company_number = r.company_number",
                   "recall": 0.7
               }
           }
       ],
       "threshold": 0.8
   }
   

5. Building and running the DAG

Once you’ve defined all your steps, you can build and run the complete DAG.

Example

from matchbox.client.dags import DAG

# Create the DAG
my_dag = DAG()

# Add index steps
my_dag.add_steps(i_companies, i_exporters, i_importers)

# Add dedupe steps
my_dag.add_steps(dedupe_companies, dedupe_exporters, dedupe_importers)

# Add link steps
my_dag.add_steps(link_exp_imp, link_companies_traders)

# Visualise the DAG
print(my_dag.draw())

# Run the entire DAG
my_dag.run()

# Alternatively, run from a specific step
# my_dag.run(start="dedupe_exporters")

The key methods for working with DAGs are:

• .add_steps(): Add one or more steps to the DAG
• .draw(): Visualise the DAG structure
• .run(): Execute the entire DAG or from a specific step

Visualising DAG Execution

When you run a DAG, Matchbox provides real-time status information:

Output

⏸️ deterministic_ch_hmrc
└── ⏸️ naive_companieshouse_companies
│   └── ⏸️ companieshouse.companies
└── ⏸️ deterministic_exp_imp
    └── ⏸️ naive_hmrc_exporters
    │   └── ⏸️ hmrc.trade__exporters
    └── ⏸️ naive_hmrc_importers
        └── ⏸️ hmrc.trade__importers

...

⏸️ deterministic_ch_hmrc
└── ⏸️ naive_companieshouse_companies
│   └── ⏸️ companieshouse.companies
└── 🔄 deterministic_exp_imp
    └── ✅ naive_hmrc_exporters
    │   └── ✅ hmrc.trade__exporters
    └── ⏭️ naive_hmrc_importers
        └── ⏭️ hmrc.trade__importers

...

✅ deterministic_ch_hmrc
└── ✅ naive_companieshouse_companies
│   └── ✅ companieshouse.companies
└── ✅ deterministic_exp_imp
    └── ✅ naive_hmrc_exporters
    │   └── ✅ hmrc.trade__exporters
    └── ✅ naive_hmrc_importers
        └── ✅ hmrc.trade__importers

Status indicators:

• ⏸️ Awaiting execution
• 🔄 Currently executing
• ✅ Completed
• ⏭️ Skipped

Advanced use cases

Multi-source linking

You can link across multiple sources in a single step:

Example

# Link Companies House data with both exporters and importers
link_ch_traders = LinkStep(
    left=StepInput(
        prev_node=dedupe_companies,
        select={companies_house: ["company_name", "postcode"]},
        cleaning_dict={
            "company_name": f"lower({companies_house.f('company_name')})",
            "postcode": companies_house.f("postcode"),
        },
    ),
    right=StepInput(
        prev_node=link_exp_imp,
        select={
            importers: ["company_name", "postcode"],
            exporters: ["company_name", "postcode"],
        },
        cleaning_dict={
            "company_name": f"""
                coalesce(
                    lower({exporters.f('company_name')}), 
                    lower({importers.f('company_name')})
                )
            """,
            "postcode": f"""
                coalesce(
                    {exporters.f('postcode')}, 
                    {importers.f('postcode')}
                )
            """,
        },
    ),
    name="deterministic_ch_hmrc",
    description="Link Companies House to HMRC traders",
    model_class=DeterministicLinker,
    settings={
        "left_id": "id",
        "right_id": "id",
        "comparisons": [
            """
                l.company_name = r.company_name
                    and l.postcode = r.postcode
            """
        ],
    },
    truth=1.0,
)

This example demonstrates how you can:

1. Use the results of a previous linking step as input
2. Select fields from multiple sources in a single step
3. Use SQL functions like coalesce() in your cleaning expressions to handle data from multiple sources
4. Create unified field names for comparison across sources

Best practices

1. Data preparation

Data cleaning is 90% of any record matching problem.

• Clean your data before matching
• Create appropriate indexes on your database tables
• Test your cleaning functions on sample data

2. Pipeline design

• Break complex matching tasks into smaller steps
• Use appropriate batch sizes for large sources
• Create clear, descriptive names for your steps

3. Execution

• Start with small samples to test your pipeline
• Monitor performance and adjust batch sizes accordingly
• Use the draw() method to visualize and debug your DAG

Conclusion

The Matchbox DAG API provides a powerful framework for building sophisticated data matching pipelines. By combining different types of steps (index, dedupe, link) with appropriate cleaning operations and matching algorithms, you can solve complex entity resolution problems efficiently.

For more information, explore the API reference for specific components:

• DAG API
• Linkers
• Results