Query execution time with small batches vs entire input set
Every time you make a call to a remote system (Neo4J or ArangoDB or any database) there is overhead in making the connection, sending the data, and then after executing your command, tearing down the connection.
What you're doing is trying to find the 'sweet spot' for your implementation as to the most efficient batch size for the type of data you are sending, the complexity of your query, the performance of your hardware, etc.
What I recommend doing is writing a test script that sends the data in varying batch sizes to help you determine the optimal settings for your use case.
I have taken this approach with many systems that I've designed and the optimal batch sizes are unique to each implementation. It totally depends on what you are doing.
See what results you get for the overall load time if you use batch sizes of 100, 1000, 2000, 5000, and 10000.
This way you'll work out the best answer for you.
I'm using ArangoDB 3.9.2 for search task. The number of items in dataset is 100.000. When I pass the entire dataset as an input list to the engine - the execution time is around ~10 sec, which is pretty quick. But if I pass the dataset in small batches one by one - 100 items per batch, the execution time is rapidly growing. In this case, to process the full dataset takes about ~2 min. Could you explain please, why is it happening? The dataset is the same.
I'm using python driver "ArangoClient" from python-arango lib ver 0.2.1
PS: I had the similar problem with Neo4j, but the problem was solved using transactions committing with HTTP API. Does the ArangoDB have something similar?