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Query a Federated Database Instance

On this page

  • Querying Data on S3
  • Querying Data on Azure Blob Storage
  • Querying Data in Your Atlas Cluster
  • Querying Data in Your Online Archives and Data Lake Datasets
  • Querying Data at a HTTP or HTTPS URL
  • Running Federated Queries
  • Configuring Query Limits
  • Troubleshooting

You can use the MongoDB Query Language (MQL) on Atlas Data Federation to query and analyze data on your data store. Atlas Data Federation supports most, but not all the standard server commands. To learn more about the supported and unsupported MongoDB server commands and aggregation pipleline stages, see Supported MongoDB Commands.

To query data on your data store, your federated database instance storage configuration must contain settings that define:

  • Your federated database instance store.

  • Federated Database Instance virtual databases and collections that map to your federated database instance store.

You can create or update your federated database instance storage configuration for your data store using the Atlas UI Visual Editor or JSON Editor, Atlas Data Federation CLI commands, and Atlas Data Federation API endpoints. To learn more about federated database instance storage configuration, see Define Data Stores for a Federated Database Instance.

Atlas Data Federation creates the virtual databases and collections you specified in your federated database instance configuration for the data in your data store. When you connect to your federated database instance and run queries, Atlas Data Federation processes your queries against the data and returns the query results. You can, optionally, configure limits on the amount of data that Atlas Data Federation processes for your queries to control costs.

Note

Atlas Data Federation uses columnar storage that doesn't preserve the order of fields within documents. Therefore, Atlas Data Federation doesn't support queries that are field-order sensitive, such as an embedded document equality query or sorting on a document field.

A database user must have one of the following roles to run queries against a federated database instance:

You can run up to 30 simultaneous queries on your federated database instance against:

  • Data in your S3 bucket or Azure Blob Storage container.

  • Documents in your MongoDB Atlas cluster.

  • Extracted data in Atlas Data Lake datasets.

  • Data in files hosted at publicly accessible URLs.

Tip

See:

The following sections contain information pertaining to running queries against data in your data store.

When deploying your federated database instance, if you specified an S3 bucket with both read and write permissions or AWS S3 s3:PutObject permission, you can also save your query results in your S3 bucket using $out to S3.

If you successfully create or update an object on your S3 data store, Data Federation returns the latest version of that object for any subsequent read requests and all list operations of the objects also reflect the changes. If your query contains multiple stages, each stage receives the most recent data available from the data store as that stage is processed.

By default, Atlas Data Federation does not return documents in any specific order for queries on Data Federations for S3 data stores. Atlas Data Federation reads the partitions concurrently and the underlying storage response order determines which documents Atlas Data Federation returns first, unless you define order using $sort in your query. For example, if you run the same findOne() query twice, you could see different documents, and if you use $skip, different documents might be skipped if $sort is not used in the query.

You incur "Data Processed" costs for the amount of data that Atlas Data Federation processes to return results for your queries in addition to the "Data Returned" cost for the amount of data that Atlas Data Federation returns. For example, for a 10 GB file, you incur the following "Data Processed" cost in addition to the "Data Returned" cost:

  • If you have no partitions, Atlas Data Federation reads the entire file to return results for the query. Therefore, you incur 10 GB of "Data Processed" cost.

  • If you have 10 partitions of 1 GB each, Atlas Data Federation targets and reads a single partition. Therefore, you incur 1 GB of "Data Processed" cost.

You can configure query limits per federated database instance and for all federated database instances in your project to limit the amount of processed data. To learn more, see Manage Atlas Data Federation Query Limits.

Note

Data partitioning doesn't guarantee reduced data processing cost. For example, if you run a blank $match query, which queries all the data, Atlas Data Federation needs to read the entire collection to return the results for the query regardless of the number of partitions.

When deploying your federated database instance, you can specify an Azure Blob Storage container with both read and write permissions.

You incur "Data Processed" costs for the amount of data that Atlas Data Federation processes to return results for your queries in addition to the "Data Returned" cost for the amount of data that Atlas Data Federation returns. For example, for a 10 GB file, you incur the following "Data Processed" cost in addition to the "Data Returned" cost:

  • If you have no partitions, Atlas Data Federation reads the entire file to return results for the query. Therefore, you incur 10 GB of "Data Processed" cost.

  • If you have 10 partitions of 1 GB each, Atlas Data Federation targets and reads a single partition. Therefore, you incur 1 GB of "Data Processed" cost.

You can configure query limits per federated database instance and for all federated database instances in your project to limit the amount of processed data. To learn more, see Manage Atlas Data Federation Query Limits.

When you run queries against your Atlas cluster through your federated database instance, Atlas Data Federation sets the appName, when querying your cluster, based on the appName that you used to connect to your federated database instance. For example, if you connect to your federated database instance with appName set to myApp (i.e. appName = "myApp"), Atlas Data Federation sets the appName when connecting to your cluster to the following:

atlas-data-federation|myApp

If you query a collection in Atlas Data Federation that is mapped to only one Atlas collection, Atlas Data Federation acts as a proxy and forwards your query to Atlas. When acting as a proxy, Atlas Data Federation doesn't scan data into its virtual collection to process the query thus improving performance and reducing cost. This optimization is not available for queries on Atlas Data Federation collections that are mapped to multiple Atlas collections.

Example

Consider the following federated database instance storage configuration:

{
"stores" : [
{
"name" : "atlas-store",
"provider": "atlas",
"clusterName": "myCluster",
"projectId": "5e2211c17a3e5a48f5497de3"
}
],
"databases" : [
{
"name" : "atlas-db",
"collections" : [
{
"name" : "foo",
"dataSources" : [
{
"storeName" : "atlas-store",
"database" : "myFooData",
"collection" : "foo"
}
]
},
{
"name" : "barbaz",
"dataSources" : [
{
"storeName" : "atlas-store",
"database" : "myBarData",
"collection" : "bar"
},
{
"storeName" : "atlas-store",
"database" : "myBazData",
"collection" : "baz"
}
]
}
]
}
]
}

For the above storage configuration, Atlas Data Federation acts as a proxy for queries on foo collection and forwards the queries to Atlas. This performance and cost optimization is not available for queries on barbaz collection because barbaz is mapped to multiple Atlas collections.

You can also save your query results in your Atlas cluster using $out to Atlas.

If you successfully create or update a document in your collection on the Atlas cluster, Data Federation returns the latest version of that document for any subsequent read requests and all list operations of the collection also reflect the changes. If your query contains multiple stages, each stage receives the most recent data available from the data store as that stage is processed.

Atlas logs your queries against your cluster data in the Atlas cluster audit logs. The log entry for a database user is in the following format:

<SERVICE_NAME>-<CUSTOMER_DATA_LAKE_NAME>-<DATABASE_USER_NAME>

For example, for a database user configured in Atlas as "user" : "CN=atlasDataLake-DataLake0-test_datalake0", a log entry in the Atlas cluster audit log looks similar to the following:

{
"atype" : "authenticate",
"ts" : { "$date" : "2022-04-29T13:17:54.020+00:00" },
"local" : { "ip" : "XXXX", "port" : 27017 },
"remote" : { "ip" : "XXXXX", "port" : 10844 },
"users" : [ { "user" : "CN=atlasDataLake-DataLake0-test_datalake0", "db" : "$external" } ],
"roles" : [ { "role" : "backup", "db" : "admin" }, { "role" : "readWriteAnyDatabase", "db" : "admin" }, { "role" : "clusterMonitor", "db" : "admin" }, { "role" : "enableSharding", "db" : "admin" }, { "role" : "atlasAdmin", "db" : "admin" }, { "role" : "dbAdminAnyDatabase", "db" : "admin" } ],
"param" : { "user" : "CN=atlasDataLake-DataLake0-test_datalake0", "db" : "$external", "mechanism" : "MONGODB-X509" },
"result" : 0
}

Note

The connection mechanism is always MONGODB-X509 in the Atlas cluster audit logs.

For queries, Atlas Data Federation uses the partitions that you created on fields during the creation of the Atlas Online Archive or Atlas Data Lake pipeline. The order of fields in the partitions is important in the same way as it is for Compound Indexes. Data is optimized for queries by the first field, followed by the second field, and so on. Atlas Data Federation parses the partitions in order; if a query omits a particular partition, Atlas Data Federation is less efficient in making use of any partitions that follow the omitted partition.

Atlas Data Federation is less performant in supporting queries on fields that don't have partitions. Atlas Data Federation doesn't support queries on fields that were explicitly excluded when creating the Atlas Data Lake pipeline.

Note

Preview

The support for HTTP data stores is available as a Preview feature. The feature and the corresponding documentation may change at any time during the Preview stage.

Data Federation also creates one partition for each URL in your collection. When you connect to your federated database instance and run queries, Data Federation processes your queries against the data and returns the query results.

You can use Atlas Data Federation to query and analyze a unified view of data in your Atlas cluster, S3 bucket or Azure Blob Storage container, HTTP URL, and Data Lake datasets. For federated queries, your federated database instance storage configuration must contain the settings that define:

  • Your S3 or Azure, Atlas, Atlas Data Lake, and HTTP stores.

    Note

    Atlas Data Federation doesn't support federated queries across cloud providers. Therefore, you can't run federated queries against data stored on AWS S3 buckets and Azure Blob Storage containers. Regardless of the cloud provider backing your Atlas cluster, you can run federated queries against data on your Atlas cluster and AWS S3 bucket or Azure Blob Storage container simultaneously.

  • Federated Database Instances with virtual collections that map to your S3 or Azure, Atlas, Atlas Data Lake, and HTTP stores.

You can create or update your federated database instance storage configuration using the Atlas UI Visual Editor or the JSON Editor, Atlas Data Federation CLI commands, and Atlas Data Federation API endpoints. To learn more about federated database instance storage configuration, see Define Data Stores for a Federated Database Instance.

When you connect to your federated database instance and run federated queries, Data Federation combines data from your Atlas cluster, S3 bucket or Azure Blob Storage container, Data Lake dataset, and HTTP URLs in virtual databases and collections and returns a union of data in the results.

You can limit the amount of data that Atlas Data Federation processes for your queries to control costs. To limit the amount of data that Atlas Data Federation processes for your queries, you can configure query limits per federated database instance or for all federated database instances in your project. When the amount of processed data reaches any applicable configured limit, Atlas Data Federation won't execute any new queries and returns an error to the client application that a limit has been reached. To learn more, see Manage Atlas Data Federation Query Limits.

Error: We are currently experiencing increased query processing wait times for Atlas Data Federation. Our Engineering team is investigating. Normal service will resume shortly, please try again.

Atlas Data Federation returns this error only when Atlas Data Federation can't execute queries because of resource contention. We recommend that you run your queries again.

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