Summary

This is an assessment of current performance of CPS operations, as of August 2023.

Since January 2023, performance of many CPS and NCMP operations have been increased by orders of magnitude - some highlights:

• NCMP CM-handle de-registration moved from average quadratic to linear time (constant rate). De-registering 20,000 CM-handles is thousands of times faster now.

• CPS Path Queries have moved from worst-case quadratic to linear time complexity. Queries that took days previously take minutes or seconds now.

• Memory usage during read, query, and update operations has been decreased by more than 90% (10 times less memory).

• Not only has performance increased, but reliability and robustness. It is now possible for a single batch operation to handle tens of thousands of elements, where it would have failed before.

CPS Core Performance

Test Environment

Test setup

The performance tests are written in Groovy (a JVM language). The tests were executed from Intelli-J using the Amazon Corretto 17 JDK.

As all CPS Core operations are synchronous, the results here are to be considered as single-threaded performance only.

Test data

Test data uses the Open ROADM YANG model - a real-world model for optical devices. Specifically, openroadm-device nodes, each consisting of 86 fragments, are created. For example, a test that creates 1,000 device nodes will result in 86,000 fragments in the database. Some tests use up to 3,000 device nodes (258,000 fragments - equivalent to around 20,000 CM-handles in NCMP), with four anchors replicating the data, meaning that the system has been tested up to 1 million fragments.

Storing data nodes

A varying number of Open ROADM device nodes will be stored using CpsDataService::saveData.

Graph of time taken to store device nodes

Open

Observations

• Storing data nodes has linear time complexity (as expected).

• Raw performance is roughly 3,500 fragments per second for the given test setup.

• Performance can be improved by enabling write batching (CPS-1795)

• There are edge cases with exponential complexity (adding books to the bookstore model).

Commentary

The current database schema does not allow for Hibernate to use JDBC write batching. There is worke in progress to address this.

JPA/Hibernate is not well-suited to tree-structured data. As an example, writing data nodes / fragments happens in two low-level steps:

• SQL INSERT to create the fragments

• SQL UPDATE to set the parent IDs of those created fragments

• meaning each created fragment requires two DB operations

There is work in progress to address this behavior using a custom algorithm using JDBC. The use of a graph database would implicitly fix such issues.

See here: https://vladmihalcea.com/the-best-way-to-map-a-onetomany-association-with-jpa-and-hibernate/ for explanation on why this occurs. Basically, unidirectional mapping OneToMany suffers from poor performance due to the order in which Hibernate writes out the entities. Switching to either unidirectional ManyToOne, or bidirectional OneToMany and ManyToOne could solve the issue using pure Hibernate, but they come with drawbacks. (To Do: More details on this.)

Updating data nodes

NOTE: I have removed the previous test results here, as the test analysis was flawed. Analysis is ongoing as per CPS-1674.

Performance tests to support new analysis are provisionally available at: https://gerrit.nordix.org/c/onap/cps/+/19086

Updating data leaves

In this scenario, 1,000 OpenROADM device nodes are already defined. The data leaves of a number of these existing data nodes will be updated using CpsDataService::updateNodeLeaves.

Example JSON payload for updating data leaves for one device:

Test Results

Graph of updating data leaves of device nodes

Open

Observations

• Updating data leaves has linear time complexity.

• Raw performance is around 1,500 fragments per second.

• This is very fast compared to updating whole data nodes, and should be preferred where possible.

Commentary

The performance of this function is excellent, and yet it may be improved by write batching.

 I recommend that NCMP use this API for updating CM-handle state.

Deleting data nodes

In this scenario, 300 OpenROADM device nodes are already defined. A number of these data nodes will be deleted using CpsDataService::deleteDataNodes. The types of nodes will be varied, for example, deleting container nodes, list elements, or whole lists.

Test results

Observations

• Delete performance is linear on the amount of data being deleted (as expected).

• Raw performance of deleting container nodes is around 35,000 fragments per second. (So we can delete data nodes around 10x faster than creating them.)

• Deleting lists is much slower than deleting the parent container of the list (this can be improved).

• Of note, attempting to delete non-existing data nodes takes longer than actually deleting the equivalent amount of nodes with descendants - it is a slow operation.

Suggested improvement: For whole list deletion, add a condition to the WHERE clause in the SQL for deleting lists, to narrow the search space to children of the parent. For example:

DELETE FROM fragment WHERE (existing conditions)

AND parent_id = (SELECT id FROM fragment WHERE xpath = '/parent-xpath')

This should narrow the performance gap in this case.

Reading data nodes

In these tests, a varying number of Open ROADM devices are created and retrieved.

Reading top-level container node

In this test, CpsDataService::getDataNodes is used to retrieve the top-level container node.

Test results

Reading the top-level container node with no descendants:

The above data clearly indicates constant time.

Reading the top-level container node with all descendants:

e

Graph of time taken to read top-level container node with all descendants

Open

Observations

• Reading a single top-level container node with no descendants has constant time (as expected).

• Reading a single top-level container node with all descendants has linear time (as expected).

• Raw performance of reading with all descendants is roughly 100,000 fragments per second.

Commentary

This is the fastest operation in CPS, in terms of fragments per second. This is not surprising, as a simple read should be the fastest operation.

Reading data nodes for multiple xpaths

This test uses CpsDataService::getDataNodesForMultipleXpaths with all descendants to retrieve a varying number of Open ROADM device nodes.

Test results

Open

Observations

• Reading many data nodes with all descendants has linear time (as expected).

• Raw performance of reading many with all descendants is roughly 80,000 fragments per second.

Additional test cases: Reading container node versus list

Recently, functionality was added to enable reading whole lists (CPS-1696). Here we compare performance of reading a container node containing a list, versus reading the list (with all descendants).