Hi,

I have 2 questions hopefully you can help with.

1. I'd like to know if the bulkArraySize internal parameter is applicable to CDC as we are trying to maximise a client's performance during periods in which the source database experiences high volumes of changes. I've seen articles like this one. However, it looks like it only mentions that it is for Full Load and not for CDC.

2. My second question is can we enable "Copy redo logs to a temporary folder" and increase the number of concurrent ASM read threads at the same time? Is it redundant to do both at the same time?

For context, this Qlik Community article mentions that enabling the setting "Copy redo logs to a temporary folder" on the Oracle source endpoint should increase performance. Can I stack these over one another to increase performance?

Help with this is appreciated. Thank you.

Kind regards,

Mohammed

We have a busy source table and have lots of transaction every minutes. However we don't see the table gets replicated under CDC into target. Source is MySQL 8.0.45 on premise while target endpoint is Snowflake AWS.

We have enabled target apply to verbose however it's not showing up as target apply in the logfiles. It only has source_capture.

23898836: 2026-09-15T12:17:52:393625 [SOURCE_CAPTURE ]T: Event header: type=16, timestamp=2026-09-15 12:17:52 (1789489072), event_len=31, pos=1069454637, next_pos=1069454668 (mysql_endpoint_capture.c:1195)
23898836: 2026-09-15T12:17:52:393625 [SOURCE_CAPTURE ]V: Event = 16, file_suffix=8666 (mysql_endpoint_capture.c:3072)
23898836: 2026-09-15T12:17:52:393625 [SOURCE_CAPTURE ]V: Resuming=0, EventPosInFile=1069454637, EventHeaderNextPos=1069454668 (mysql_endpoint_capture.c:3097)
23898836: 2026-09-15T12:17:52:393625 [SOURCE_CAPTURE ]V: Event timestamp '2026-09-15 12:17:52' (1789489072), pos 1069454637 (mysql_endpoint_capture.c:3148)
23898836: 2026-09-15T12:17:52:393625 [SOURCE_CAPTURE ]T: > XID_EVENT (mysql_endpoint_capture.c:3201)
23898836: 2026-09-15T12:17:52:393625 [SOURCE_CAPTURE ]V: Send COMMIT (7) record, record id -1795445488, stream position '$.008666:1069454637:-1:1069454668:37221256041509:$.008666:1069454456' (mysql_endpoint_capture.c:3722)
23898836: 2026-09-15T12:17:52:393625 [SOURCE_CAPTURE ]V: read_next_binlog_event: next_pos=1069454668, curr_file=LUSQTTNDB04A-bin.008666, curr_file_len=1069454668, last_file=LUSQTTNDB04A-bin.008666, last_len=1069454668 (mysql_endpoint_capture.c:973)
23898836: 2026-09-15T12:17:52:393625 [SOURCE_CAPTURE ]V: Wait for new events... (mysql_endpoint_capture.c:981)
23898836: 2026-09-15T12:17:52:409223 [SOURCE_CAPTURE ]T: Wait minimum 5 seconds for next poll (mysql_endpoint_capture.c:994)

Hi,

I read a community post which said that uncommitted transactions on the source would result in source latency to increase on the task until the transaction is complete. However, I tested this by creating an uncommitted transaction on the source Oracle database yet I was unable to recreate this behaviour. I am on QR 2025.11.0.437. Why is this happening? Is this the expected behavior?

Help with this is appreciated. 

Thank you,

Mohammed

So we trying to load data in s3 bucket in CSV format but i can see dates are not getting populated in CSV soo i have applied global rule but than it is changing the date 

1 transformation rule 
Transformation

Transformation Scope

Transformation Action

Source: Oracle 11g / Target: MySQL 5.7. We are executing Change Data Capture (CDC) across 7 tasks.
All tasks were previously running in Batch optimized apply mode.

Over the past three weeks, performance degradation occurred on the target database, so we implemented tuning measures such as adding indexes and modifying RDS parameters, which yielded a certain level of performance improvement.
However, despite these performance optimizations, 1 of the 7 tasks continued to encounter target replication latency of over 1 hour that lasted for several consecutive hours.

As an experiment, we changed the replication mode of the delayed task from Batch mode to Transactional apply mode.
As a result, the replication latency for this task improved rapidly and was completely resolved. At the same time, the performance load on the target database decreased, and application response times improved noticeably.

Following these results, we changed another task that was running in Batch mode to Transactional mode as well. This further reduced the load on the target database, and application responsiveness improved dramatically.

Since changing these two tasks to Transactional apply mode, the previous performance problems disappeared as if they were never there, and the multiple reported application response issues were resolved all at once.

＜Questions＞
According to the Qlik Replicate documentation, Batch optimized apply mode is the default and is generally recommended for significantly faster processing speed compared to Transactional apply mode. What could be the potential reason why switching to Transactional apply mode dramatically reduced and eliminated replication latency in our environment?

What could be the reason why changing the replication mode for only 2 out of 7 tasks resulted in a lower load on the target database and resolved application response issues across the system?

If there are similar known technical advisory cases or specific points/metrics we should review or verify, we would greatly appreciate your insights and guidance.

Hi Qlik Community,

I am attempting to integrate Qlik Cloud Automation with Slack Event Subscriptions to handle @mention queries using Qlik Answers.

However, I am stuck at the Slack Event Subscriptions url_verification step and unable to achieve Verified status.

Setup Overview:

1. Trigger: Triggered Automation (Start block set to Webhook/Triggered).

2. Verification Logic:

   • Condition block checks if Start > body > challenge is not empty.

   • YES branch: Routes directly to an Output block returning Start > body > challenge, immediately followed by a Stop block.

3. Authentication:

   • Slack Event Subscriptions does not allow sending custom HTTP headers (such as -H "X-Execution-Token: <TOKEN>").

   • Therefore, I am appending the token as a query parameter in Slack's Request URL: https://<tenant>[.qlikcloud.com/api/v1/automations/](https://.qlikcloud.com/api/v1/automations/)<automation-id>/actions/execute?X-Execution-Token=<TOKEN>

What Works:

• Sending a POST request via curl with the token in the HTTP Header works perfectly and returns the challenge string: curl -X POST "<URL>" -H "X-Execution-Token: <TOKEN>" -H "Content-Type: application/json" -d "{\"type\": \"url_verification\", \"challenge\": \"test_123\"}"

The Problem:

• When testing with query parameter authentication (which Slack relies on), the request fails with an HTTP 403 Forbidden error: {"errors":[{"code":"HTTP-403","title":"No permission.","detail":"You don't have sufficient permissions to access this resource."}]}

• We confirmed this 403 Forbidden response using Google Apps Script / UrlFetchApp when sending the token via query parameter (?X-Execution-Token=<TOKEN>).

Questions:

1. Does Qlik Cloud Automation support executing Triggered Automations when the X-Execution-Token is passed via URL query parameters (?X-Execution-Token=...) instead of HTTP Headers?

2. Is there a specific setting or URL format required to allow Slack Event Subscriptions to authenticate successfully without custom HTTP headers?

Any advice or workarounds would be greatly appreciated. Thank you!

Hi everyone, I’m working on a Qlik Sense application that loads a large dataset, and the reload process has become increasingly slow as the data volume grows. I’ve already optimized some joins and removed unnecessary fields, but the reload time is still high.                                      aarpmembership com

What are the best practices for optimizing Qlik Sense load scripts when working with large datasets? Are there specific techniques around incremental loads, joins, resident loads, or data model design that could significantly improve reload performance?

Any suggestions or examples would be greatly appreciated.

Hello

I want to create an Nprinting report not based on an existing table in Qlik app
the report is very simple
I want to add 4 dimensions and an expression
I know that the expression is created in teh formulas node on the left
but how can I add dimensions to the report (excel format)
Kindly advise

also is there a way to reference a master measure in Nprinting?