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.
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:
Source
s: indexing data from sourcesModel
s: Removing duplicates within one data input, or linking two data inputs. As data inputs,Model
s can takeSource
s or otherModel
s.
Source
s and Model
s can form Query
objects, which allow you to retrieve the version of the data implied by that DAG step. Querying a source gives you the records in that source, and querying from a model gives you the deduplicated or linked records at that point in the DAG. When querying from a model, you need to specify which sources you want to query from that model’s lineage.
source: Source
deduper: Model
# ... define your source and a model deduplicating it ...
source_query = source.query()
model_query = deduper.query(source)
Model
s are formed from Query
objects.
other_source: Source
# ... define your second source ...
deduper = source.query().deduper(...)
linker = deduper.query().linker(other_source.query())
All these objects are lazy: they don’t actually retrieve any data unless you run them, for example:
The steps need to be run in order, but once you’ve finalised your DAG, it’s better to automatically run all of them using a single DAG command, as is shown later. When you run a step, either directly or through the DAG, its data is cached so that running it again won’t do anything, unless you force a re-run.
Setting up your environment¶
Before building a pipeline, it’s worth configuring logging:
import logging
# 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)
You will also need to define the engine to read your data sources:
1. Defining a new DAG¶
You’re now ready to create your first DAG
.
A DAG needs a name, which will be used to identify this DAG once you publish it to the Matchbox server. You also need to use the .new_run()
method to prepare the DAG to send results to the server.
This DAG will own all the sources and models you define later.
2. Defining data sources¶
Now you can 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.
from matchbox.client import RelationalDBLocation
# Companies House data
companies_house = dag.source(
name="companies_house",
location=RelationalDBLocation(name="dbname", client=engine),
extract_transform="""
select
number::str as company_number,
company_name,
upper(postcode) as postcode,
from
companieshouse.companies;
""",
infer_types=True,
index_fields=["company_name", "company_number", "postcode"],
key_field="company_number",
)
# Exporters data
exporters = dag.source(
name="hmrc_exporters",
location=RelationalDBLocation(name="dbname", client=engine),
extract_transform="""
select
id,
company_name,
upper(postcode) as postcode,
from
hmrc.trade__exporters;
""",
infer_types=True,
index_fields=["company_name", "postcode"],
key_field="id",
)
Each Source
object requires:
- A
location
, such asRelationalDBLocation
. This will need aname
, andclient
- The name of a location is a way of tagging it, such that later on you can filter sources 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 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
- These must be found in the result of the
- A key field (
key_field
) that uniquely identifies each record- This must be found in the result of the
extract_transform
logic
- This must be found in the result of the
3. Creating dedupers¶
Dedupe steps identify and resolve duplicates within a single source.
```python from matchbox.client.models.dedupers.naive import NaiveDeduper
dedupe_companies_house = companies_house.query( cleaning={ “company_name”: f”lower({companies_house.f(‘company_name’)})”, } ).deduper( name=”naive_companieshouse_companies”, description=”Deduplication based on company name”, model_class=NaiveDeduper, model_settings={ “unique_fields”: [“company_name”], }, truth=1.0, )
A query can optionally take instructions on how to clean the data. These are defined using a dictionary where:
- the dictionary key is the desired column name that will be output
- the dictionary value is a SQL expression in DuckDB format
A deduper takes:
- A unique
name
for the step - An optional
description
explaining the purpose of the step - The deduplication algorithm to use (
model_class
) - Configuration settings (
model_settings
) for the algorithm - Optionally, a
truth
threshold (a float between0.0
and1.0
) above which a match is considered “true”. By default, this is set to1.0
. This value is only relevant when using a model that can output matches with different confidence scores, which is not the case for aNaiveDeduper
.
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={
"company_name": f"lower({companies_house.f('company_name')})",
# company_number not cleaned, so needs qualification later
}
model_settings={
"unique_fields": [
"company_name",
companies_house.f("company_number"), # Qualified!
],
}
# Clean everything for consistency
cleaning = {
"company_name": f"lower({companies_house.f('company_name')})",
"company_number": companies_house.f("company_number") # Just aliasing
}
model_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 steps, 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?
A Query
extracts data to a columnar format. Models will often query the same column names from multiple sources, so column names must be qualified with their source.
For example,
will return a dataframe with the following columns:
id
(the Matchbox ID)companies_house_company_number
companies_house_company_name
companies_house_postcode
Note how the fields specified are “qualified” with the source they came from. When defining cleaning instructions, we need to refer to qualified source names too. Source.f()
is provided as a convenient way to select fields qualified by a source.
The rules for the cleaning dictionary are:
- 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:
Note:
- How we qualify the field we clean
- How we alias it to
company_name
company_number
andpostcode
aren’t mentioned.
The columns output by the query will be:
id
(the Matchbox ID)companies_house_company_number
company_name
(unqualified)companies_house_postcode
Finally, cleaned fields typically need specifying in a model. Here’s our example:
model_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.
If you want to test and improve your cleaning dictionary iteratively, but don’t want to re-run a full query each time, you can do:
old_cleaning = ...
# Store inside the query object the raw data
query = source.query(cleaning=old_cleaning, cache_raw=True)
query.run()
new_cleaning = ...
# Will apply new cleaning without re-fetching the data, and also update the query
# configuration with the new cleaning
query.clean(new_cleaning)
4. Creating link steps¶
Link steps connect records between different sources.
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 = dedupe_exporters.query(
exporters,
cleaning={
"company_name": f"lower({exporters.f('company_name')})",
"postcode": exporters.f("postcode"),
},
).linker(
dedupe_importers.query(
importers,
cleaning={
"company_name": f"lower({importers.f('company_name')})",
"postcode": importers.f("postcode"),
},
)
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
"""
],
},
)
A linker requires:
- A second query which represents the data to link on the right side
- A unique
name
for the step - An optional
description
explaining the purpose of the step - The linking algorithm to use (
model_class
) - Configuration (
model_settings
) for the algorithm - An optional
truth
threshold (a float between0.0
and1.0
) above which a match is considered “true”, the default being1.0
.
As with deduplication, the cleaning
dictionary maps field aliases to DuckDB SQL expressions that can reference input columns. See On cleaning for how to specify this functionality.
Available linker types¶
Matchbox provides several linking methodologies:
-
DeterministicLinker
: Links records based on exact matches of specified fields -
WeightedDeterministicLinker
: Assigns different weights to different comparison fields -
SplinkLinker
: Uses probabilistic matching with the Splink libraryfrom 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) ] ) model_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. Running and publishing the DAG¶
Once you’ve defined all your steps, you can run and store the results of your DAG
.
Once you’re happy with your results, you need to publish your DAG so that other users can query from it.
Visualising DAG execution¶
When you run a DAG, Matchbox provides real-time status information:
⏸️ 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:
# Link Companies House data with both exporters and importers
link_ch_traders = dedupe_companies_house.query(
companies_house,
cleaning={
"company_name": f"lower({companies_house.f('company_name')})",
"postcode": companies_house.f("postcode"),
},
).linker(
link_exp_imp.query(
importers,
exporters
cleaning={
"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
"""
],
},
)
This example demonstrates how you can:
- Use the results of a previous linking step as input
- Select fields from multiple sources in a single step
- Use SQL functions like
coalesce()
in your cleaning expressions to handle data from multiple sources - Create unified field names for comparison across sources
Re-run a previous DAG¶
You might want to publish a new run of your DAG based on newer data. You can retrieve the old DAG and inspect it. You can’t sync or publish it, as it will be read-only. However, you can generate a new run from it explicitly
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: