Hi Vinayak,

Would you be able to build JanusGraph from master branch and try again? The upcoming 0.6.0 release contains many optimizations which might be helpful. 

Without knowing more details of your use case (your queries, your loading script, your JanusGraph configs, your JanusGraph metrics, your Cassandra metrics), it’s very hard to give any concrete suggestion. Anyway, I would strongly recommend you try out the master version first and see how it goes.

Best,

Boxuan

「Vinayak Bali <vinayakbali16@...>」在 2021年7月26日 週一，下午3:55 寫道：

Hi All, 

I am using janusgraph for a while. The use case which I am working on consists of 1.5 million nodes and 3 million edges. Prepared a batch loading groovy script. The performance of the data loading script is as follows: 

Nodes: 5 mins

Edges: 13 mins

Total: 18 mins

Also, the count query including edges takes mins to execute. 

Both Janusgraph(0.5.2) and Cassandra are installed on the same instance.

 

Hardware Configuration:

RAM: 92 GB

Cores: 48 

I want expert suggestions/steps which can be followed to improve the performance. Request you to share your thoughts regarding the same.

Thanks & Regards,

Vinayak

There's a BUILDING file with instructions in the repo.
 
 
 

Sent: Monday, July 26, 2021 at 10:31 AM
From: "Vinayak Bali" <vinayakbali16@...>
To: janusgraph-users@...
Subject: Re: [janusgraph-users] Performance Improvement

Hi Boxuan, 
 
Thank you for your response. I am not sure, how I can build janusgraph from the master branch. If you can share step's/procedure to do the same, I can check otherwise need to wait for the new release. 
 
My use case consists of a single node label and self-relation between them. You consider it as BOM in the supply chain. 
The janusgraph and Cassandra configurations are the same which are set as default while installing.
 
The data loading script takes the CSV files as input, divides the files into different batches, and loads the batches using multi-threading. If you need more details, I can share a generic script with you and also the metrics. 
 
Thanks & Regards,
Vinayak 

Hi Vinayak,

0.6.0 version of JanusGraph is released. I posted some quick tips to improve throughput to your CQL storage here:
https://lists.lfaidata.foundation/g/janusgraph-users/message/6148
I also had a post in LinkedIn with links to relative documentation parts and several better suggestions about internal ExecutorServices usage here: https://www.linkedin.com/posts/porunov_release-060-janusgraphjanusgraph-activity-6840714301062307840-r6Uw

In 0.6.0 you can improve your CQL throughput drastically using a simple configuration `storage.cql.executor-service.enabled: false` which I definitely recommend to do but you should properly configure throughput related configurations.

Best regards,
Oleksandr

Hi Vinayak,

I didn't follow your statements about count but I just want to add that if you don't use mixed index for count query than your count will require iteratively returning each element and count them in-memory (i.e. very inefficient). To check if your count query is using mixed index you can use `profile()` step.

I also noticed that you say that you need to return all properties for all vertices / edges. If so, you may consider using multiQuery which will return properties for your vertices faster than valueMap() step in certain cases. The only thing you need to consider when using `multiQuery` (actually any query) is tx-cache size (don't confuse with database cache). In case your tx-cache size is too small to hold all the vertices than some vertices' properties will be evicted from the cache. Thus, when you will try to return values for the vertex properties it will make new database calls to retrieve those properties. In the worst case all your access to properties may lead to separate database calls. To eliminate this downside you need to make sure that your transaction cache size is at least the same amount of vertices your are accessing (or bigger). In such case `multiQuery().addAllVertices(yourVertices).properties()` will return all properties for all vertices and it will hold those properties in-memory instead of evicting them.

Moreover, it looks like your use cases are read-heavy and not write-heavy. You may improve your performance by making sure all your writes are using consistency-level=ALL and all your reads are using consistency-level=ONE. You may want to disable consistency check as well as internal / external checks for your transactions if you are sure about your data. It will make some of your queries faster but less safer.

You also need to make sure that you configured your CQL driver throughput optimally for your load. In case your JanusGraph is embedded into your Application you need to make sure your application has the smallest latency between your Cassandra nodes (you may even consider placing your application to the same nodes with Cassandra or just moving your Gremlin Server to those nodes and use remote connection).

There are many JanusGraph and CQL driver configurations which you may use to tune your performance for your use-case. This topic is to broad to give all-fits solution. Different use cases might need different approaches. I would strongly recommend you to explore all JanusGraph configurations here: https://docs.janusgraph.org/configs/configuration-reference/ . It will allow to configure your general JanusGraph configuration, your transactions configuration, and your CQL driver configuration much better if you are aware about all the configurations. For advanced CQL configuration options see this configurations here: https://docs.datastax.com/en/developer/java-driver/4.13/manual/core/configuration/reference/  (storage.cql.internal in JanusGraph).

You may also try exploring other storage backends which may give you smaller latency (hence better performance), like ScyllaDB, Aerospike, etc.

Best regards,
Oleksandr Porunov