Batch API Execution

In batch API executions, requests to ArangoDB server are stored in client-side in-memory queue, and committed together in a single HTTP call. After the commit, results can be retrieved later from BatchJob objects.

Example:

from python_aioarango import ArangoClient, AQLQueryExecuteError

# Initialize the ArangoDB client.
client = ArangoClient()

# Connect to "test" database as root user.
db = await 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.
async 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 = await batch_col.insert({'_key': 'Kris'})
    job2 = await batch_col.insert({'_key': 'Rita'})
    job3 = await batch_aql.execute('RETURN 100000')
    job4 = await batch_aql.execute('INVALID QUERY')  # Fails due to syntax error.

# Upon exiting context, batch is automatically committed.
assert await students.has('Kris', check_rev=False)
assert await students.has('Rita', check_rev=False)

# 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 cursor.next() == 100000

# If a job fails, the exception is propagated up during result retrieval.
try:
    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)
await batch_db.collection('students').insert({'_key': 'Jake'})
await batch_db.collection('students').insert({'_key': 'Jill'})

# The commit must be called explicitly.
await batch_db.commit()
assert 'Jake' in students
assert 'Jill' in students

Note

• 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.