Introduction: Unlocking IBM Data Replication CDC’s Best-Kept Secret
If you’ve dabbled in data replication with CDC (Change Data Capture), you know the basics — capturing changes and keeping data synchronized. But beneath the surface lies a feature that doesn’t get nearly the attention it deserves: Shared Scrape. Think of it as the backstage manager, silently ensuring that all your data subscriptions work in perfect harmony. Whether dealing with heavy data loads or unpredictable delays, Shared Scrape steps in, minimizing resource usage and streamlining replication with zero manual intervention.
Prerequisite Note: This blog is for those who have a good grasp of IBM Data replication CDC tool and data replication in general but want to go deeper into how Shared Scrape, feature of IBM data replication product, can change the game. Even if you know the basics, prepare to uncover new efficiencies!
Understanding Shared Scrape: A Seamless Replication Experience
Shared Scrape is like having a smart assistant for your CDC. It creates a shared “staging store” — a central cache where parsed log records are kept. Any subscription that needs these records can simply pick them up from this shared cache, eliminating the need to read and parse logs separately. The result? Lower CPU consumption, fewer resources, and smoother data replication.
But here’s where it gets even more interesting: the staging store isn’t unlimited. Its size is controlled by a “disk quota” set during instance creation. As new changes pile up, the oldest data in the staging store is deleted to keep it within the quota. If a subscription falls too far behind and its required data gets removed, it gets “kicked out” and must switch to using its own private log reader and parser, increasing the database load.
The beauty of Shared Scrape is its automatic switching. When a subscription’s data falls outside the bounds of the shared staging store, it seamlessly shifts to private mode — no manual intervention required. This intelligent switching ensures minimal disruptions, even when subscriptions are out of sync.
To keep Shared Scrape running at its best, all subscriptions should operate at a similar pace. If one subscription lags or stops, it could get out of sync and cause performance issues. In essence, Shared Scrape offers an elegant balance — optimizing resource use and dynamically adapting to changing needs.
Why Shared Scrape Matters: Reducing Database Overhead
Shared Scrape isn’t just another feature; it’s a smarter way of managing multiple subscriptions without sacrificing performance. When dealing with complex data replication scenarios, Shared Scrape steps in to balance resource use, reduce redundancy, and keep your operations running smoothly. Let’s explore how it changes the game:
Centralized Staging Store: One Source for All Subscriptions
Instead of having each subscription pull data independently, Shared Scrape uses a centralized staging store to keep parsed log records accessible to everyone. This shared cache ensures that subscriptions are always pulling data from a single source, cutting down on redundant operations and reducing the load on the database.
Smoother Transitions: Automatic Mode Switching
Shared Scrape’s real strength is its ability to dynamically adapt. When a subscription is aligned with the staging store’s bounds, it seamlessly uses shared resources. But when it falls out of bounds — either lagging behind or jumping too far ahead — it automatically switches to private mode. No manual intervention needed, no complex configurations — just intelligent, real-time adjustments.
Managing Disk Quota: Ensuring the Cache Stays Within Limits
The staging store’s size is governed by a disk quota set by the user. As new log data is captured, older entries are purged to stay within this limit. If a subscription relies on data that gets deleted, it’s kicked out of the shared mode and must switch to its own private log reader. This smart management of the cache ensures that the staging store is always optimized, preventing it from becoming a bottleneck.
Keeping Subscriptions in Sync: The Coordination Advantage
Shared Scrape is most effective when all subscriptions are running at a similar pace. When one subscription lags significantly behind, it risks being pushed out of the shared cache. To avoid disruptions, it’s best to keep subscriptions aligned, allowing Shared Scrape to coordinate data access smoothly and efficiently.
The Real Impact: Efficiency, Adaptability, and Reduced Overhead
With Shared Scrape, CDC handles replication challenges with ease, automatically adjusting to changing scenarios while minimizing resource consumption. It’s not just about handling logs more efficiently — it’s about maintaining harmony, even as the replication workload grows.
The Shared Scrape Workflow: How It Keeps Subscriptions Aligned
Shared Scrape is designed to be the default mode for efficient data replication in CDC, dynamically managing all active subscriptions to keep them in sync. But the real secret lies in how it tracks each subscription’s bookmark — the exact point in the log it’s processing — and adjusts the shared cache to accommodate them. Let’s explore how Shared Scrape uses bookmarks to maintain alignment and what happens when you need stricter control over the workflow.
Centralized Staging Store: Synchronizing with Bookmarks
At the core of Shared Scrape is the staging store — a shared cache that holds parsed log records, making them accessible to every active subscription. Each subscription has a bookmark that indicates where it is in the log and what changes it needs to replicate. Shared Scrape continuously monitors these bookmarks to determine if a subscription can continue using the shared cache or if it should switch to private mode.
If a subscription’s bookmark is within the staging store’s bounds, it pulls data from the shared cache, eliminating the need to parse logs separately. This prevents multiple subscriptions from competing for log access and reduces CPU usage. Because Shared Scrape is enabled by default, each subscription is initially aligned to the shared mode, maintaining smooth replication without any parameter configurations.
Managing Misalignment: Switching Automatically When Bookmarks Fall Out of Bounds
A subscription can fall out of the shared cache’s bounds in two ways: by falling behind or by jumping ahead. When this happens, Shared Scrape seamlessly switches the affected subscription to a private log reader until it catches up. This prevents the slower or out-of-sync subscription from disrupting others, allowing the majority to keep using shared resources.
For example, if Subscription “A” lags because it paused temporarily while others kept running, its bookmark might drop below the earliest record in the staging store. When it restarts, it can’t use the shared cache and will switch to a private reader until it catches up. Conversely, if Subscription “B” jumps ahead due to a sudden bulk refresh, it will temporarily operate in private mode until Shared Scrape’s cache catches up to its position.
Strict Mode with staging_store_can_run_independently=false: Prioritizing the Slowest Subscription
By default, Shared Scrape handles misalignment gracefully, allowing subscriptions to switch between shared and private modes as needed. But what if you want to enforce stricter control? Setting staging_store_can_run_independently=false forces all subscriptions to stick to shared mode, even when some fall behind.
In this strict mode, Shared Scrape prioritizes the slowest subscription, making faster subscriptions wait until the slowest catches up. This prevents any subscription from getting out of sync but comes at a cost — slower overall replication speeds. Faster subscriptions might end up idle, waiting for the slowest one to process its data before they can proceed. This setting is ideal when you need absolute synchronization across all subscriptions, ensuring that they all consume data at the same pace.
Recovering Alignment: Returning to Shared Mode Automatically
When a subscription that was kicked out catches up and its bookmark re-aligns with the shared cache, Shared Scrape automatically transitions it back to shared mode. This return to alignment is seamless and ensures that the shared cache is utilized to its fullest, maintaining efficiency and reducing the load on the system.
Avoiding Common Pitfalls: When Shared Scrape Fails to Perform
While Shared Scrape offers significant benefits in most scenarios, there are situations where it can become a bottleneck rather than a boost. Understanding these pitfalls and knowing how to address them is crucial for maintaining optimal performance. Let’s look at some common issues and how to prevent Shared Scrape from failing to deliver the expected efficiency.
1. Misalignment of Subscriptions: The Fragmentation Trap
One of the most common pitfalls is subscription misalignment. When one subscription lags significantly behind or surges far ahead, it risks falling out of the shared cache’s bounds. This causes Shared Scrape to kick that subscription into private mode, leading to fragmented replication workflows. The result? Increased CPU consumption and potential data processing delays.
How to Avoid It: Monitor subscription bookmarks regularly and ensure that all subscriptions are operating at a similar pace. If you notice repeated misalignment, consider fine tuning disk quota so that there is more space available for slower subscription to stay in shared mode.
2. Over-Reliance on Strict Mode: Slowing Down Faster Subscriptions
Using staging_store_can_run_independently=false to enforce strict alignment can sometimes create more problems than it solves. While this mode ensures that all subscriptions consume data at the same pace, it also means that the slowest subscription dictates the speed of replication for all others. Faster subscriptions end up waiting, causing delays and under-utilization of resources.
How to Avoid It: Use strict mode only when absolute synchronization is necessary. In most cases, allowing Shared Scrape to handle the switching automatically will lead to better overall performance.
3. Disk Quota Mismanagement: Losing Critical Data
It’s important to consider how the size of the staging store can impact performance. If a subscription falls behind and its required data is purged due to limited disk quota, it will be forced into private mode, causing a spike in CPU usage.
How to Avoid It: Make sure to configure your staging store size appropriately based on the data volume and the speed of your subscriptions. A balanced cache size helps prevent unnecessary purging and keeps most subscriptions in shared mode.
4. Unplanned Table Refreshes: Causing Bookmark Jumps
Refreshing large tables or making bulk changes can cause a subscription’s bookmark to jump ahead suddenly, making it fall out of the shared cache’s bounds. This forces Shared Scrape to treat the subscription as an outlier, switching it to private mode temporarily until the staging store catches up.
How to Avoid It: Coordinate large table refreshes across all subscriptions to ensure that they remain within the same range of the shared cache. This minimizes disruptions and helps maintain alignment.
5. Long Pauses: Subscriptions Falling Too Far Behind
Stopping a subscription for an extended period while others keep running can cause its bookmark to fall significantly behind the shared cache’s earliest position. When this happens, the subscription is kicked out and will need to use its own resources to catch up. This not only increases CPU consumption but also slows down replication until it re-aligns.
How to Avoid It: Avoid stopping individual subscriptions unless absolutely necessary. If a pause is required, try to refresh the subscription to keep it up to date.
