How to increase AeroSpark read performance? - performance

Question

I am using latest AeroSpark connector to work with AeroSpike and Spark ML. But when i have inserted round 60M records to AeroSpike, i got too big time amount in read operations. For example for fetch round 500K records from set that contains 60M records, AeroSpark spend ~30 mins. When i look at htop cmd output, AeroSpike use only 7% of CPU.
Each record round contains 1k of data. The AeroSpike and Spark hosted on the same node. The data filtered by secondary index.
How can i speed up performance in read operations? Seems AeroSpark is working only by one thread, how i can parallelize this job? Any suggestions?
AeroSpike conf:
memory-size 8G
default-ttl 30d
storage-engine device {
file /vol/rmla.data
filesize 900G
}

Answer 1

Without knowing anything about your server, and with just a snippet of config, I'll stick to some generic recommendations that should improve your experience.
Disk IO
You are clearly bound by the read speed from your storage media, which you declared to be a file. If you're storing the data on disk, you can either use file or device in the storage-engine device config block.
There is a big difference in the read and write latency between a file on a HDD versus raw device access to an SSD. Typically Aerospike is used with data stored on enterprise-grade SSD devices. Read the section in the operations manual about initializing and setting up the drive. Declaring multiple devices for the namespace with give you a linear performance boost (two drives will have double the read and write throughput of one of the same kind).
In Amazon EC2 you could use the c3, i2, r3, or i3 instance families for this purpose. The ephemeral SSD devices of EC2 instances don't need to be over-provisioned, have their RAID turned off, etc. They only need to be initialized before they're first used. Do not use EBS drives for primary storage, as they're too slow.
Cluster Configuration
The Spark connector uses lots of scan operations. Make sure that you've configured scan-threads under your service config block to the number of cores. If you don't know how many cores you have, do cat /proc/cpuinfo. If Spark is the only client using the Aerospike cluster, you can tune the scan threads higher.
Connector Configuration
You can modify the connector config options for lower write latency. Optionally set aerospike.commitLevel to CommitLevel.COMMIT_MASTER.
Upgrade Version
As of November 28 2016 aerospike/aerospark supports Spark 2.0. Make sure you're using the latest code.
Note: See the new tutorial for Aerospark on the Aerospike website.