qlikreplicate

Background

The business is doing their best to keep me on my toes with Qlik Replicate; finding new was to bend and stretch the system and consequently my sanity.

The initial request was, “Can we overwrite this field in a Qlik Replicate task with a SOURCE_LOOKUP?”

OK – we can do this.  I abhor putting ETL logic in Qlik Replicate tasks and wanting to keep them as simple as possible and allow the power and the flexibility of the downstream systems to manipulate data.

But project timelines were pressing, and I complied with their request.

Later, they came back to me and requested a to add a filter to the derived field in question.

And that led to me and our Tech Business analyst scratching our heads. 

If we apply a filter to our focus field; will it use the raw field that is in the table? Or will is use the new lookup field with the same name to base the filter on?

Testing the filters – setting up

To start with; some simple tables in MS-SQL:

CREATE TABLE dbo.TEST_LOOKUP
(
	ACCOUNT_ID INT PRIMARY KEY,
	FRUIT_ID INT,
	FRUIT_NAME VARCHAR(100),
	SOURCE_NAME VARCHAR(100)
);

GO

CREATE TABLE dbo.FRUITS
(
	FRUIT_ID INT PRIMARY KEY,
	NEW_FRUIT_NAME VARCHAR(100)
)

GO

INSERT INTO dbo.FRUITS VALUES(1, 'NEW APPLES');
INSERT INTO dbo.FRUITS VALUES(2, 'NEW ORANGES');

A simple Qlik Replicate task was created to replicate from the table dbo.TEST_LOOKUP.

All columns were brought across instead of FRUIT_NAME. FRUIT_NAME will be overwritten with the source lookup:

source_lookup('NO_CACHING','dbo','FRUITS','NEW_FRUIT_NAME','FRUIT_ID =?',$FRUIT_ID)

To test; a simple insert was added to ensure that the source lookup is working correctly:

INSERT INTO dbo.TEST_LOOKUP VALUES(1, 1, 'OLD APPLES', 'Truck');

Result:

{
    "magic": "atMSG",
    "type": "DT",
    "headers": null,
    "messageSchemaId": null,
    "messageSchema": null,
    "message": {
        "data": {
            "ACCOUNT_ID": 1,
            "FRUIT_ID": 1,
            "SOURCE_NAME": "Truck",
            "FRUIT_NAME": "NEW APPLES"
        },
        "beforeData": null,
        "headers": {
            "operation": "INSERT",
            "changeSequence": "20240529060703760000000000000000005",
            "timestamp": "2024-05-29T06:07:03.767",
            "streamPosition": "0071a49f:000f8e09:001c",
            "transactionId": "6EDBA1FA0E0000000000000000000000",
            "changeMask": "0F",
            "columnMask": "0F",
            "transactionEventCounter": 1,
            "transactionLastEvent": true
        }
    }
}

Everything is working correctly; FRUIT_NAMES got overwritten with “NEW APPLES” in the json output.

Testing the filters – placing bets

In the CDC task; a new filter was added:

$FRUIT_NAME == 'NEW ORANGES'

And the following SQL statement was run on the source system:

INSERT INTO dbo.TEST_LOOKUP VALUES(2, 2, 'OLD ORANGES', 'Fridge');

So – If Qlik Replicate filters on the base table’s field; the change WILL NOT be replicated through.

Likewise if Qlik Replicate is using the new derived field for filter; the change WILL come through.

And the results are…

{
    "magic": "atMSG",
    "type": "DT",
    "headers": null,
    "messageSchemaId": null,
    "messageSchema": null,
    "message": {
        "data": {
            "ACCOUNT_ID": 2,
            "FRUIT_ID": 2,
            "SOURCE_NAME": "Fridge",
            "FRUIT_NAME": "NEW ORANGES"
        },
        "beforeData": null,
        "headers": {
            "operation": "INSERT",
            "changeSequence": "20240529061434050000000000000000065",
            "timestamp": "2024-05-29T06:14:34.050",
            "streamPosition": "0071a49f:000f901a:0005",
            "transactionId": "28DCA1FA0E0000000000000000000000",    
            "changeMask": "17",
            "columnMask": "17",
            "transactionEventCounter": 1,
            "transactionLastEvent": true
        }
    }
}

Qlik will use the derived source lookup field over the original field in the table.

Conclusion

Once again this highlights the danger of putting ETL code into Qlik Replicate tasks. It obscures business rules and can lead to confusion in operations like the scenario above.

It is best to use Qlik Replicate to get the data out of the source database as quickly and as pure as possible and then use the power of the downstream systems to manipulate the data.

Continuing our ongoing Qlik Replicating story of trying to replicate a DB2/zOS database to AWS RDS Postgres.

We made small improvements; but nothing substantial to reach the TPS for which we were aiming.  I was at my experience end of what I knew and decided to reach for professional help.

We have a support relationship with IBT; who helped us out with the initial set up QR in our organisation.  But recently we have been self-resolving our own problems and have not been using their help.  Now this was suitable time to ask for their help.

IBT has always been helpful when we have asked for assistance.  Another handy aspect with the relationship is that IBT has a quick support relationship with Qlik.  If they don’t know the answer; they can get the answer easily from Qlik.

IBT asked us to collect the usual data; diagnostic logs, source and target DB metrics and QR server core metrics.  Nothing looked under duress, so IBT dived into the nitty and gritty details of the diagnostic packs.

Their techs noticed that our outgoing stream buffers were full.  This means that the changes were coming in faster than were getting sent out to the destination.  IBT suggested to try increasing the size of the outgoing stream.

Without going through the details of this step; here is a Qlik knowledge base article on Increasing the outgoing stream queue.

“She’s breaking up – I can’t hold her”

We started off with:

"stream_buffers_number" : 5,
"stream_buffer_size" : 10,

It was a marginal improvement.  Measured in a thimble full of performance improvement.  Still nowhere near the TPS we needed. 

IBT asked us to increase the two variables in small increments of “stream_buffers_number” + 5 and “stream_buffer_size” + 10.  With each increase there was a minuscule improvement.

 But more worrying with each increase; the QR task was using more memory to the point that increasing the buffer size was unsustainable with the resources on the server.  Even if increasing the buffer variables and the gained TPS was linear relationship; we would need a very beefy server to reach 300 TPS.

So again, it was a little gain; and with all the added “Little gains” over the past few fix iterations we were still no closer to our needed 300 TPS.

Increasing the buffer variables might be helpful if you are close to your TPS and trying to get over the last hurdle.  But since we’re so far behind; we had to look for another solution.

If you are new to this story; pick up the introduction from here 

50 TPS – that’s not good…

Our Stress and Volume Testing (SVT) did not turn out that great; which was an understatement. We were aiming for a minimum of 300tps, but we were only achieving 50tps.

Because we could not run the SVT process within business hours as the DB2 databases for production and development shares the same hardware; I had to write a script to simulate the changes on a MS-SQL server.

This means we could use an idle Dev MS-SQL box and run SVT testing whenever we needed it. With the new testing script, I tried various iterations of settings under Change Processing Tuning to see if there was an optimal setting. I could not get above 60tps.

Meanwhile the rest of the team contacted AWS support to see if they can see anything obvious.

AWS Support recommended using the Postgres utility “pg_bench” from various locations to work out if there is a bottle neck between On Prem and AWS RDS. 

They recommend running the benchmark for 15 minutes with different numbers of clients.

Armed with the pg_bench utility; I started performing benchmark runs against the AWS RDS database from various locations in our network, trying to work out where the bottle neck is.

Running the tests

Our network had several locations that we could run pg_bench on to assess the tps to AWS RDS.  To be more specific; to an AWS RDS Proxy.  

To start off we had to learn how to use the command first.  The benchmark database must be initialised with some tables and data.  This is done with the following command:

pgbench --host rds-database-proxy.aws.com --port 5432 --username qlikreplicate -i -s 50 dest_db

Once done; a 15min benchmark was run with the following command:

pgbench --host rds-database-proxy.aws.com --port 5432 --username qlikreplicate -l -c 2 -j 2 -T 900 dest_db

Parameters:

Four tests were conducted:

1. The OnPrem Qlik Replicate server to AWS RDS Proxy
2. An EC2 machine to AWS RDS Proxy
3. The OnPrem Qlik Replicate server to an OnPrem Postgres database.  (Actually to our Enterprise manager analytic database.  So it is not a fancy highly optimised database)
4. An EC2 machine directly to the AWS RDS database

Why a AWS RDS Proxy?  I’m not sure of the story behind why there was a proxy between Qlik and RDS; rumours it was because the security team didn’t initially allow our On Prem services to connect directly to cloud database.  They insisted that a proxy between the services and databses.

Results

The pg_bench tests revealed several learnings to the team. But unfortunately, there were no answers to our QR latency problem.

It was a bit of grim relief it proved that QR was not the cause of our latency problem; there is a component between us and AWS RDS database that is causing the issue.

Even more unfortunate when I raise the issue to the network team thinking that there might be an IPS, QOS device that might be slowing us down; they could not find anything. I have been informed that the bandwidth between our On Prem and AWS is broad and fast and there should not be any obvious chocking the tps. Grumpily I mused that households can now stream multiple 4K videos at home without a frame dropped while we can’t send a few database changes from our datacentre to an AWS database at reasonable speeds

Interestingly the RDS proxy was a significant bottleneck when we started questioning its purpose in the pipeline. Even the AWS support team was scratching their heads of the purpose of it in the use case we had. Later on, I tried OnPrem to the direct RDS; but that made no difference to the tps. 

Lessons learnt

• Validate poor latency through Qlik Replicate via another tool.  In this instance pg_bench took QR right out of the equation
• Don’t use AWS RDS proxy for Qlik Replicate unless you have a critical use case.  Performance test thoroughly your design choices