Batch API Execution


The batch request API is deprecated since ArangoDB 3.8.0. We discourage its use, as it will be removed in a future release. It is already slow and seems to regularly create weird errors when used with recent versions of ArangoDB.

The driver functionality has been refactored to no longer use the batch API, but a ThreadPoolExecutor instead. For backwards compatibility, max_workers is set to 1 by default, but can be increased to speed up batch operations. Essentially, the batch API can now be used to send multiple requests in parallel, but not to send multiple requests in a single HTTP call. Note that sending multiple requests in parallel may cause conflicts on the servers side (for example, requests that modify the same document).

To send multiple documents at once to an ArangoDB instance, please use any of arango.collection.Collection methods that accept a list of documents as input, such as:

After the commit, results can be retrieved later from BatchJob objects.


from arango import ArangoClient, AQLQueryExecuteError

# Initialize the ArangoDB client.
client = ArangoClient()

# Connect to "test" database as root user.
db = client.db('test', username='root', password='passwd')

# Get the API wrapper for "students" collection.
students = db.collection('students')

# Begin batch execution via context manager. This returns an instance of
# BatchDatabase, a database-level API wrapper tailored specifically for
# batch execution. The batch is automatically committed when exiting the
# context. The BatchDatabase wrapper cannot be reused after commit.
with db.begin_batch_execution(return_result=True) as batch_db:

    # Child wrappers are also tailored for batch execution.
    batch_aql = batch_db.aql
    batch_col = batch_db.collection('students')

    # API execution context is always set to "batch".
    assert batch_db.context == 'batch'
    assert batch_aql.context == 'batch'
    assert batch_col.context == 'batch'

    # BatchJob objects are returned instead of results.
    job1 = batch_col.insert({'_key': 'Kris'})
    job2 = batch_col.insert({'_key': 'Rita'})
    job3 = batch_aql.execute('RETURN 100000')
    job4 = batch_aql.execute('INVALID QUERY')  # Fails due to syntax error.

# Upon exiting context, batch is automatically committed.
assert 'Kris' in students
assert 'Rita' in students

# Retrieve the status of each batch job.
for job in batch_db.queued_jobs():
    # Status is set to either "pending" (transaction is not committed yet
    # and result is not available) or "done" (transaction is committed and
    # result is available).
    assert job.status() in {'pending', 'done'}

# Retrieve the results of successful jobs.
metadata = job1.result()
assert metadata['_id'] == 'students/Kris'

metadata = job2.result()
assert metadata['_id'] == 'students/Rita'

cursor = job3.result()
assert == 100000

# If a job fails, the exception is propagated up during result retrieval.
    result = job4.result()
except AQLQueryExecuteError as err:
    assert err.http_code == 400
    assert err.error_code == 1501
    assert 'syntax error' in err.message

# Batch execution can be initiated without using a context manager.
# If return_result parameter is set to False, no jobs are returned.
batch_db = db.begin_batch_execution(return_result=False)
batch_db.collection('students').insert({'_key': 'Jake'})
batch_db.collection('students').insert({'_key': 'Jill'})

# The commit must be called explicitly.
assert 'Jake' in students
assert 'Jill' in students


  • Be mindful of client-side memory capacity when issuing a large number of requests in single batch execution.

  • BatchDatabase and BatchJob instances are stateful objects, and should not be shared across multiple threads.

  • BatchDatabase instance cannot be reused after commit.

See BatchDatabase and BatchJob for API specification.