Reading Large Data Instrumentation Comparisons

Setup

Environment:

OS: Zorin OS 16.2

RAM: 32 GB

CPU: Intel® Core™ i7-10610U CPU @ 1.80GHz × 8

Data: 

Included in ZIP file (at bottom)

1. All data under 1 anchors
   1. Under /openroadm-devices we have list of 10,000 openroadm-device[..]
2. tree-size per 'device' fragments 86 fragments
3. KB per devices: 333 KB

Single-large object request

Query: cps/api/v1/dataspaces/openroadm/anchors/owb-msa221-anchor/node?xpath=/openroadm-devices/openroadm-device[@device-id='C201-7-13A-5A1']&include-descendants=true

Durations are average of 100 measurements

 (1 object out of many)

Observations (patch 3) 

1. Is 'findByAnchorAndCspPath' being used (shouldn't?!)
2. Query time increases until list-size reached 6,000 elements and then levels off

Whole data tree as one request

1 object containing all node as descendants (mainly one big list)

Query: cps/api/v1/dataspaces/openroadm/anchors/owb-msa221-anchor/node?xpath=/openroadm-device&include-descendants=true

All queries ran 10-reames

*Only initial Hibernate query, hibernate will lazily fetch data later which is reflected in E2E time

Observations:

1. PathsSet #2  did perform better than the latest patch! Need to compare Daniel Hanrahan will follow up

Get nodes parallel

Fetch 1 device from a database with 10,000 devices

Bash parallel Curl commands, 1 thread executed 10 Sequential requests with no delays, average response times are reported

Query: cps/api/v1/dataspaces/openroadm/anchors/owb-msa221-anchor/node?xpath=/openroadm-devices/openroadm-device[@device-id='C201-7-13A-5A1']&include-descendants=true

Patch: https://gerrit.onap.org/r/c/cps/+/133511/12

Graphs:

1. Average E2E Execution Time
2. Internal Method Counts (total)

Observations

1. From 10 Parallel request (of 10 sequential request) the client can't always connect and we see time out error (succes ratio <100%)
   1. Sequential request are fired faster than actual responses so from DB perspective they are almost parallel request as well 
2. Database probably already become bottleneck with 2 threads, effectively firening a total of 20 call very quickly. Its know that the DB connection pool/internal will slow down from 12 or more 'parallel' request

Get 1000 nodes in Parallel with varying thread count

In this test, 1000 requests are sent using curl, but with varying thread count (using --parallel-max option).

echo -e "Threads\tTime"
for threads in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 30 40 50; do
	echo -n -e "$threads\t"
	/usr/bin/time -f "%e" curl --silent --output /dev/null --fail --show-error \
		--header "Authorization: Basic Y3BzdXNlcjpjcHNyMGNrcyE=" \
		--get "http://localhost:8883/cps/api/v1/dataspaces/openroadm/anchors/owb-msa221-anchor/node?xpath=/openroadm-devices/openroadm-device\[@device-id='C201-7-[1-25]A-[1-40]A1'\]&include-descendants=true" \
		--parallel --parallel-max $threads --parallel-immediate
done

Note the above curl command performs 1000 requests. It is based on globbing in the URL - curl allows ranges such as [1-25]  in the URL, for example:

  http://example.com/archive[1996-1999]/vol[1-4].html

which would expand into a series of 16 requests to:

• http://example.com/archive1996/vol1.html
• http://example.com/archive1996/vol2.html
• ...
• http://example.com/archive1999/vol4.html

Results

Observations

• There were no failures during the tests (e.g. timeouts or refused connections).
• Performance increases nearly linearly with increasing thread count, up to the number of CPU cores.
• Performance stops increasing when the number of threads equals the number of CPU cores (expected).
• Verbose statistics show that each individual request takes around 0.14 seconds, regardless of thread count (but with multiple CPU cores, requests are really done in parallel).

Data sheets