MongoDB - MongoDB CRUD Operations, Bulk Write Operations

Overview

MongoDB provides clients the ability to perform write operations in bulk. Bulk write operations affect a singlecollection. MongoDB allows applications to determine the acceptable level of acknowledgement required for bulk write operations.

New in version 3.2.

The  method provides the ability to perform bulk insert, update, and remove operations. MongoDB also supports bulk insert through the .

Ordered vs Unordered Operations

Bulk write operations can be either ordered or unordered.

With an ordered list of operations, MongoDB executes the operations serially. If an error occurs during the processing of one of the write operations, MongoDB will return without processing any remaining write operations in the list. See 

With an unordered list of operations, MongoDB can execute the operations in parallel, but this behavior is not guaranteed. If an error occurs during the processing of one of the write operations, MongoDB will continue to process remaining write operations in the list. See .

Executing an ordered list of operations on a sharded collection will generally be slower than executing an unordered list since with an ordered list, each operation must wait for the previous operation to finish.

By default,  performs ordered operations. To specify unordered write operations, set ordered : false in the options document.

See 

bulkWrite() Methods

 supports the following write operations:

Each write operation is passed to  as a document in an array.

For example, the following performs multiple write operations:

The characters collection contains the following documents:

{ "_id" : 1, "char" : "Brisbane", "class" : "monk", "lvl" : 4 },
{ "_id" : 2, "char" : "Eldon", "class" : "alchemist", "lvl" : 3 },
{ "_id" : 3, "char" : "Meldane", "class" : "ranger", "lvl" : 3 }

The following  performs multiple operations on the collection:

try {
   db.characters.bulkWrite(
      [
         { insertOne :
            {
               "document" :
               {
                  "_id" : 4, "char" : "Dithras", "class" : "barbarian", "lvl" : 4
               }
            }
         },
         { insertOne :
            {
               "document" :
               {
                  "_id" : 5, "char" : "Taeln", "class" : "fighter", "lvl" : 3
               }
            }
         },
         { updateOne :
            {
               "filter" : { "char" : "Eldon" },
               "update" : { $set : { "status" : "Critical Injury" } }
            }
         },
         { deleteOne :
            { "filter" : { "char" : "Brisbane"} }
         },
         { replaceOne :
            {
               "filter" : { "char" : "Meldane" },
               "replacement" : { "char" : "Tanys", "class" : "oracle", "lvl" : 4 }
            }
         }
      ]
   );
}
catch (e) {
   print(e);
}

The operation returns the following:

{
   "acknowledged" : true,
   "deletedCount" : 1,
   "insertedCount" : 2,
   "matchedCount" : 2,
   "upsertedCount" : 0,
   "insertedIds" : {
      "0" : 4,
      "1" : 5
   },
   "upsertedIds" : {

   }
}

For more examples, see 

Strategies for Bulk Inserts to a Sharded Collection

Large bulk insert operations, including initial data inserts or routine data import, can affect  performance. For bulk inserts, consider the following strategies:

Pre-Split the Collection

If the sharded collection is empty, then the collection has only one initial , which resides on a single shard. MongoDB must then take time to receive data, create splits, and distribute the split chunks to the available shards. To avoid this performance cost, you can pre-split the collection, as described in .

Unordered Writes to mongos

To improve write performance to sharded clusters, use  with the optional parameter orderedset to false.  can attempt to send the writes to multiple shards simultaneously. For emptycollections, first pre-split the collection as described in .

Avoid Monotonic Throttling

If your shard key increases monotonically during an insert, then all inserted data goes to the last chunk in the collection, which will always end up on a single shard. Therefore, the insert capacity of the cluster will never exceed the insert capacity of that single shard.

If your insert volume is larger than what a single shard can process, and if you cannot avoid a monotonically increasing shard key, then consider the following modifications to your application:

• Reverse the binary bits of the shard key. This preserves the information and avoids correlating insertion order with increasing sequence of values.
• Swap the first and last 16-bit words to “shuffle” the inserts.

EXAMPLE: The following example, in C++, swaps the leading and trailing 16-bit word of BSON ObjectIds generated so they are no longer monotonically increasing.

using namespace mongo;
OID make_an_id() {
  OID x = OID::gen();
  const unsigned char *p = x.getData();
  swap( (unsigned short&) p[0], (unsigned short&) p[10] );
  return x;
}

void foo() {
  // create an object
  BSONObj o = BSON( "_id" << make_an_id() << "x" << 3 << "name" << "jane" );
  // now we may insert o into a sharded collection
}

SEE ALSO:  for information on choosing a sharded key. Also see  (in particular, ).