This template creates a batch pipeline that reads documents from MongoDB and writes them to BigQuery.
If you want to capture MongoDB change stream data, you can use the MongoDB to BigQuery (CDC) template.
Pipeline requirements
- The target BigQuery dataset must exist.
- The source MongoDB instance must be accessible from the Dataflow worker machines.
Output format
The format of the output records depends on the value of the
userOption
parameter. If userOption
is
NONE
, the output has the following schema. The
source_data
field contains the document in JSON format.
[ {"name":"id","type":"STRING"}, {"name":"source_data","type":"STRING"}, {"name":"timestamp","type":"TIMESTAMP"} ]
If userOption
is FLATTEN
, the pipeline flattens
the documents and writes the top-level fields as table columns. For example,
suppose the documents in the MongoDB collection contain the
following fields:
"_id"
(string
)"title"
(string
)"genre"
(string
)
Using FLATTEN
, the output has the following schema. The
timestamp
field is added by the template.
[ {"name":"_id","type":"STRING"}, {"name":"title","type":"STRING"}, {"name":"genre","type":"STRING"}, {"name":"timestamp","type":"TIMESTAMP"} ]
If userOption
is JSON
, the pipeline stores
the document in the BigQuery JSON format. BigQuery
has built-in support for JSON data using the
JSON data type.
For more information, see
Working with JSON data in GoogleSQL.
Template parameters
Required parameters
- mongoDbUri: The MongoDB connection URI in the format
mongodb+srv://:@.
. - database: Database in MongoDB to read the collection from. For example,
my-db
. - collection: Name of the collection inside MongoDB database. For example,
my-collection
. - userOption:
FLATTEN
,JSON
, orNONE
.FLATTEN
flattens the documents to the single level.JSON
stores document in BigQuery JSON format.NONE
stores the whole document as a JSON-formatted STRING. Defaults to: NONE. - outputTableSpec: The BigQuery table to write to. For example,
bigquery-project:dataset.output_table
.
Optional parameters
- KMSEncryptionKey: Cloud KMS Encryption Key to decrypt the mongodb uri connection string. If Cloud KMS key is passed in, the mongodb uri connection string must all be passed in encrypted. For example,
projects/your-project/locations/global/keyRings/your-keyring/cryptoKeys/your-key
. - filter: Bson filter in json format. For example,
{ "val": { $gt: 0, $lt: 9 }}
. - useStorageWriteApi: If
true
, the pipeline uses the BigQuery Storage Write API (https://cloud.google.com/bigquery/docs/write-api). The default value isfalse
. For more information, see Using the Storage Write API (https://beam.apache.org/documentation/io/built-in/google-bigquery/#storage-write-api). - useStorageWriteApiAtLeastOnce: When using the Storage Write API, specifies the write semantics. To use at-least-once semantics (https://beam.apache.org/documentation/io/built-in/google-bigquery/#at-least-once-semantics), set this parameter to
true
. To use exactly-once semantics, set the parameter tofalse
. This parameter applies only whenuseStorageWriteApi
istrue
. The default value isfalse
. - bigQuerySchemaPath: The Cloud Storage path for the BigQuery JSON schema. For example,
gs://your-bucket/your-schema.json
. - javascriptDocumentTransformGcsPath: The Cloud Storage URI of the
.js
file that defines the JavaScript user-defined function (UDF) to use. For example,gs://your-bucket/your-transforms/*.js
. - javascriptDocumentTransformFunctionName: The name of the JavaScript user-defined function (UDF) to use. For example, if your JavaScript function code is
myTransform(inJson) { /*...do stuff...*/ }
, then the function name is myTransform. For sample JavaScript UDFs, see UDF Examples (https://github.com/GoogleCloudPlatform/DataflowTemplates#udf-examples). For example,transform
.
User-defined function
Optionally, you can extend this template by writing a user-defined function (UDF) in JavaScript. The template calls the UDF for each input element. Element payloads are serialized as JSON strings.
To use a UDF, upload the JavaScript file to Cloud Storage and set the following template parameters:
Parameter | Description |
---|---|
javascriptDocumentTransformGcsPath |
The Cloud Storage location of the JavaScript file. |
javascriptDocumentTransformFunctionName |
The name of the JavaScript function. |
For more information, see Create user-defined functions for Dataflow templates.
Function specification
The UDF has the following specification:
userOption
is NONE
, the JSON object must include
a property named _id
that contains the document ID.Run the template
Console
- Go to the Dataflow Create job from template page. Go to Create job from template
- In the Job name field, enter a unique job name.
- Optional: For Regional endpoint, select a value from the drop-down menu. The default
region is
us-central1
.For a list of regions where you can run a Dataflow job, see Dataflow locations.
- From the Dataflow template drop-down menu, select the MongoDB to BigQuery template.
- In the provided parameter fields, enter your parameter values.
- Click Run job.
gcloud
In your shell or terminal, run the template:
gcloud dataflow flex-template run JOB_NAME \ --project=PROJECT_ID \ --region=REGION_NAME \ --template-file-gcs-location=gs://dataflow-templates-REGION_NAME/VERSION/flex/MongoDB_to_BigQuery \ --parameters \ outputTableSpec=OUTPUT_TABLE_SPEC,\ mongoDbUri=MONGO_DB_URI,\ database=DATABASE,\ collection=COLLECTION,\ userOption=USER_OPTION
Replace the following:
PROJECT_ID
: the Google Cloud project ID where you want to run the Dataflow jobJOB_NAME
: a unique job name of your choiceREGION_NAME
: the region where you want to deploy your Dataflow job—for example,us-central1
VERSION
: the version of the template that you want to useYou can use the following values:
latest
to use the latest version of the template, which is available in the non-dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/latest/- the version name, like
2023-09-12-00_RC00
, to use a specific version of the template, which can be found nested in the respective dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/
OUTPUT_TABLE_SPEC
: your target BigQuery table name.MONGO_DB_URI
: your MongoDB URI.DATABASE
: your MongoDB database.COLLECTION
: your MongoDB collection.USER_OPTION
: FLATTEN, JSON, or NONE.
API
To run the template using the REST API, send an HTTP POST request. For more information on the
API and its authorization scopes, see
projects.templates.launch
.
POST https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/flexTemplates:launch { "launch_parameter": { "jobName": "JOB_NAME", "parameters": { "inputTableSpec": "INPUT_TABLE_SPEC", "mongoDbUri": "MONGO_DB_URI", "database": "DATABASE", "collection": "COLLECTION", "userOption": "USER_OPTION" }, "containerSpecGcsPath": "gs://dataflow-templates-LOCATION/VERSION/flex/MongoDB_to_BigQuery", } }
Replace the following:
PROJECT_ID
: the Google Cloud project ID where you want to run the Dataflow jobJOB_NAME
: a unique job name of your choiceLOCATION
: the region where you want to deploy your Dataflow job—for example,us-central1
VERSION
: the version of the template that you want to useYou can use the following values:
latest
to use the latest version of the template, which is available in the non-dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/latest/- the version name, like
2023-09-12-00_RC00
, to use a specific version of the template, which can be found nested in the respective dated parent folder in the bucket— gs://dataflow-templates-REGION_NAME/
OUTPUT_TABLE_SPEC
: your target BigQuery table name.MONGO_DB_URI
: your MongoDB URI.DATABASE
: your MongoDB database.COLLECTION
: your MongoDB collection.USER_OPTION
: FLATTEN, JSON, or NONE.
What's next