Dumping, Restoring, Importing, and Exporting Data

    The mongodump utility creates a binary (BSON) backup of a MongoDB database. The mongodump tool is the preferred method of dumping data from your source MongoDB deployment when looking to restore it into your Amazon DocumentDB cluster due to the size efficiencies achieved by storing the data in a binary format.

    Depending on the resources available on the instance or machine you are using to perform the command, you can speed up your mongodump by increasing the number of parallel connections dumped from the default 1 using the --numParallelCollections option. A good rule of thumb is to start with one worker per vCPU on your Amazon DocumentDB cluster’s primary instance.

    The following is an example usage of the mongodump utility in the Amazon DocumentDB cluster, sample-cluster.

    The mongorestore utility enables you to restore a binary (BSON) backup of a database that was created with the mongodump utility. You can improve restore performance by increasing the number of workers for each collection during the restore with the --numInsertionWorkersPerCollection option (the default is 1). A good rule of thumb is to start with one worker per vCPU on your Amazon DocumentDB cluster’s primary instance.

    The following is an example usage of the mongorestore utility in the Amazon DocumentDB cluster, sample-cluster.

    1. mongorestore --ssl \
    2.     --host="sample-cluster.node.us-east-1.docdb.amazonaws.com:27017" \
    3.     --username=sample-user \
    4.     --password=abc0123 \
    5.     --sslCAFile rds-combined-ca-bundle.pem <fileToBeRestored>

    The mongoexport tool exports data in Amazon DocumentDB to JSON, CSV, or TSV file formats. The mongoexport tool is the preferred method of exporting data that needs to be human or machine readable.

    Note

    The following is an example usage of the mongoexport tool in the Amazon DocumentDB cluster, sample-cluster.

    1. mongoexport --ssl \
    2.     --host="sample-cluster.node.us-east-1.docdb.amazonaws.com:27017" \
    4.     --db=sample-database \
    5.     --out=sample-output-file \
    6.     --username=sample-user \
    7.     --password=abc0123 \
    8.     --sslCAFile rds-combined-ca-bundle.pem

    The mongoimport tool imports the contents of JSON, CSV, or TSV files into an Amazon DocumentDB cluster. You can use the -–numInsertionWorkers parameter to parallelize and speed up the import (the default is 1).

    The following is an example usage of the mongoimport tool in the Amazon DocumentDB cluster, sample-cluster.

    1. mongoimport --ssl \
    2.     --host="sample-cluster.node.us-east-1.docdb.amazonaws.com:27017" \
    3.     --collection=sample-collection \
    4.     --file=<yourFile> \
    5.     --numInsertionWorkers 4 
    6.     --username=sample-user 
    7.     --password=abc0123 \
    8.     --sslCAFile rds-combined-ca-bundle.pem

    The following tutorial describes how to use the mongodump, mongorestore, mongoexport, and mongoimport utilities to move data in and out of an Amazon DocumentDB cluster.

    1. Prerequisites — Before you begin, ensure that your Amazon DocumentDB cluster is provisioned and that you have access to an Amazon EC2 instance in the same VPC as your cluster. For more information, see Connect Using Amazon EC2.

      To be able to use the mongo utility tools, you must have the mongodb-org-tools package installed in your EC2 instance, as follows.

      1. sudo yum install mongodb-org-tools-4.0.18

      Because Amazon DocumentDB uses Transport Layer Security (TLS) encryption by default, you must also download the Amazon RDS certificate authority (CA) file to use the mongo shell to connect, as follows.

      1. wget https://raw.githubusercontent.com/ozlerhakan/mongodb-json-files/master/datasets/restaurant.json

    3. Import the sample data into Amazon DocumentDB — Since the data is in a logical JSON format, you will use the mongoimport utility to import the data into your Amazon DocumentDB cluster.

      1. mongoimport --ssl \ 
      2.     --host="tutorialCluster.amazonaws.com:27017" \ 
      3.     --collection=restaurants \
      4.     --db=business \ 
      5.     --file=restaurant.json \
      6.     --numInsertionWorkers 4 \
      7.     --username=<yourUsername> \
      8.     --password=<yourPassword> \

    4. Dump the data with mongodump — Now that you have data in your Amazon DocumentDB cluster, you can take a binary dump of that data using the mongodump utility.

      1. mongodump --ssl \
      2.     --host="tutorialCluster.us-east-1.docdb.amazonaws.com:27017"\
      3.     --collection=restaurants \
      4.     --db=business \
      5.     --out=restaurantDump.bson \
      6.     --username=<yourUsername> \
      7.     --password=<yourPassword> \
      8.     --sslCAFile rds-combined-ca-bundle.pem

    5. Drop the restaurants collection — Before you restore the restaurants collection in the business database, you have to first drop the collection that already exists in that database, as follows.

      1. use business

    6. Restore the data with mongorestore — With the binary dump of the data from Step 3, you can now use the mongorestore utility to restore your data to your Amazon DocumentDB cluster.

      1. mongorestore --ssl \
      2.     --host="tutorialCluster.us-east-1.docdb.amazonaws.com:27017" \
      3.     --numParallelCollections 4 \
      4.     --username=<yourUsername> \
      5.     --password=<yourPassword> \
      6.     --sslCAFile rds-combined-ca-bundle.pem restaurantDump.bson

    7. Export the data using mongoexport — To complete the tutorial, export the data from your cluster in the format of a JSON file, no different than the file you imported in Step 1.

      1. mongoexport --ssl \
      2.     --host="tutorialCluster.node.us-east-1.docdb.amazonaws.com:27017" \
      3.     --collection=restaurants \
      4.     --db=business \
      5.     --out=restaurant2.json \
      6.     --username=<yourUsername> \
      7.     --password=<yourPassword> \
      8.     --sslCAFile rds-combined-ca-bundle.pem

    8. Validation — You can validate that the output of Step 5 yields the same result as Step 1 with the following commands.

      1. wc -l restaurant.json

      Output from this command:

      1. 2548 restaurant.json