Optimising python-gremlin for fastApi
PS: Please give me some rope here as I am new to gremlin-python!
I have a fastAPI which is serving some Rest API calls based on the data that I am fetching from a Neptune instance. I am using gremlin-python library to make the queries to graph instance.
Some points to note:
1. I'm using Uvicorn workers with fastapi. You can consider a single worker for any stats I share.
2. There may be multiple graph calls in a single API request.
3. Currently getting around 8-10 RPS.
Now having worked on a lot of fastApi services, I know that this RPS is way too low. I have done lots of optimisations in the past by moving towards asyncio "native" libraries like AioHttp, AioRedis, etc. Achieving a considerable 90-100 RPS in some instances on each worker.
I have scourged through the gremlin-python source code, and am confused about a couple of things.
1. Even though the library is creating a separate loop, while doing any I/O, the coroutines are being used in a blocking manner by calling
2. I have tried exploring other libraries like
3. Is the main event being blocked when I am running the queries, For example
If the main fastapi event loop is indeed being blocked, what can I do to unblock the same when making the gremlin queries??
Any suggestions to increase the throughput on each worker are highly appreciated!!
I have a fastAPI which is serving some Rest API calls based on the data that I am fetching from a Neptune instance. I am using gremlin-python library to make the queries to graph instance.
Some points to note:
1. I'm using Uvicorn workers with fastapi. You can consider a single worker for any stats I share.
2. There may be multiple graph calls in a single API request.
3. Currently getting around 8-10 RPS.
Now having worked on a lot of fastApi services, I know that this RPS is way too low. I have done lots of optimisations in the past by moving towards asyncio "native" libraries like AioHttp, AioRedis, etc. Achieving a considerable 90-100 RPS in some instances on each worker.
I have scourged through the gremlin-python source code, and am confused about a couple of things.
1. Even though the library is creating a separate loop, while doing any I/O, the coroutines are being used in a blocking manner by calling
loop.run_until_complete2. I have tried exploring other libraries like
aiogremlinaiogoblin3. Is the main event being blocked when I am running the queries, For example
If the main fastapi event loop is indeed being blocked, what can I do to unblock the same when making the gremlin queries??
Any suggestions to increase the throughput on each worker are highly appreciated!!
Apache TinkerPop is an open source graph computing framework and the home of the Gremlin graph query language.
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