JanusGraph, a highly scalable graph database solution, supports historically Cassandra and HBase as database backends. We decided to put Scylla in the mix, certainly searching for the best performing backend. We ran test scenarios that cover high volume reads and writes. In this talk, we will show you the performance results of Scylla vs others and also share our lessons learned during the performance evaluation.

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Performance Study for
JanusGraph Storage Backends,
Scylla, Cassandra, and HBase
Chin Huang, Software Engineer, IBM
Ted Chang, Performance Engineer, IBM

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Chin Huang
Chin Huang is a software engineer at the IBM Open Technologies. He
has worked on software development, solutions integration, and
performance evaluation for open source projects such as OpenStack,
JanusGraph, and various databases.
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Ted Chang is a software engineer at IBM Open Technologies and
Design for Performance. He has worked on various enterprise and
open source cloud solutions. At the moment, his focus is JanusGraph
performance and characterization.
Ted Chang

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Agenda
▪ Overview – Graph database storage backends
▪ Performance evaluation scenarios and results
o Insert vertices (inserts)
o Insert edges (= search + update)
o Graph traversal (query)
▪ Lessons learned
▪ Q&A
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Overview – Graph
database storage
backends

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Overview
▪ JanusGraph is a highly scalable graph database optimized for storing and
querying large graphs.
▪ JanusGraph stores graphs in adjacency list format which means that a graph is
stored as a collection of vertices with their adjacency list.
▪ Data storage layer is pluggable. Most common storage backends are
Cassandra and HBase. We want to add Scylla to the mix!
▪ Test workloads:
o Insert vertices - writes
o Insert edges - reads and writes
o Queries – reads
▪ Test environments: database clusters
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Performance test environment
▪ Server spec
o Physical servers: x3650 M5, 2 sockets x 14 cores, 384 GB (12 x 32G) memory
o CPU: Intel Xeon Processor E5-2690 v4 14C 2.6GHz 35MB Cache 2400MHz
o Network interface: Emulex VFA5.2 ML2 Dual Port 10GbE SFP+ Adapter
o Disk: 720 GB SSD, RAID 5
o Operating system: Ubuntu 16.04.2 LTS
▪ Public tools
o jMeter - load testing tool
o nmon, nmon analyser - system performance monitor and analyze tool
o VisualVM - all-in-one Java troubleshooting/profiling tool
o GCeasy - garbage collection log analysis tool
o Prometheus and grafana – monitoring dashboard
▪ Home grown tools
o Graph schema loader, data generator, batch importer
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Performance Test Topology
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Cassandra
Hbase + HDFS
+ Zookeeper
Scylla
Cassandra
Hbase + HDFS
+ Zookeeper
Scylla
Cassandra
Hbase + HDFS
+ Zookeeper
Scylla
JanusGraph
Database Cluster
Load injector
queryinsert, update

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Performance
evaluation scenarios
and results

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Performance Evaluation: Insert Vertices
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▪ 40 mil vertices in total
▪ 2 properties for each vertex
▪ Insert scenario
▪ Fully utilize the injectors to
generate the loading against
the databases

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Performance Evaluation: Insert Edges
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▪ 30 mil edges in total
▪ 1 property for each edge
▪ Query and update scenario

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Performance Evaluation: Graph Traversal
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▪ Query: g.V().has('name', ‘usr_name').in('Follows').as('a').out('Retweets').in('Tweets').has('name',
‘usr_name').select('a').values('name').dedup()

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Lessons Learned
Scylla
• Easy clustering - adding multiple nodes at once
• Well self-tuned but also lacks documentation
• Even load distributed
• Fully utilize system resources
• CPU utilization mis-represents real loads
• Nice monitoring dashboard – prometheus + grafana
• Works with existing Cassandra utility clients
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Lessons Learned - Continued
Cassandra
• Cluster bootstrapping takes more efforts
• Smaller memory footprint
HBase
• Uneven CPU% on caused by hot regions
• Need to carefully configure read and write cache settings for
better throughput
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THANK YOU
chhuang@us.ibm.com
htchang@us.ibm.com
Please stay in touch
Any questions?