Modernizing Core Insurance coverage Techniques: Breaking the Batch Bottleneck

Modernizing your legacy database to Java + MongoDB Atlas doesn’t should imply sacrificing batch efficiency. By leveraging bulk operations, clever prefetching, and parallel execution, we constructed an optimization framework that not solely bridges the efficiency hole however, in lots of circumstances, surpasses legacy techniques.

In workloads the place jobs had been beforehand working 25–30x slower earlier than utilizing this framework, it introduced execution occasions again on par and, in some circumstances, delivered 10–15x higher efficiency. For world insurance coverage platforms, considerably improved batch efficiency has develop into an added technical profit to doubtlessly assist newer performance.

The modernization dilemma

For organizations modernizing their core platforms, catering to vital consumer workload and revenue-generating functions, transferring from a legacy RDBMS to a contemporary utility stack with Java + MongoDB unlocks a number of advantages:

• Versatile doc mannequin: PL/SQL code tightly {couples} enterprise logic with the database, making even small adjustments dangerous and time-consuming. MongoDB Atlas, with its versatile doc mannequin and application-driven logic, permits groups to evolve schemas and processes shortly, an enormous benefit for industries like insurance coverage, the place rules, merchandise, and buyer expectations change quickly.
• Scalability and resilience: Legacy RDBMS platforms had been by no means designed for right this moment’s scale of digital engagement. MongoDB’s distributed structure helps horizontal scale-out, guaranteeing that core insurance coverage workloads can deal with rising buyer bases, high-volume claims, and peak-time spikes with out main redesigns.
• Cloud-native by design: MongoDB is constructed to thrive within the cloud. Options like world clusters, built-in replication, and excessive availability with decreased infrastructure complexity, whereas enabling deployment flexibility throughout hybrid and multi-cloud environments.
• Fashionable developer ecosystem: Decouples database and enterprise logic dependencies, accelerating characteristic supply.
• Unified operational + analytical workloads: Fashionable insurance coverage platforms demand greater than transactional processing; they require real-time insights. MongoDB’s means to assist each operational workloads and analytics on dwell knowledge reduces the hole between claims processing and decision-making.

Nevertheless, alongside these benefits, one of many first hurdles they encounter is batch jobs efficiency, the roles that are supposed to run day by day/weekly/month-to-month, like an ETL course of.

PL/SQL thrives on set-based operations inside the database engine. However when the identical workloads are reimplemented with a separate utility layer and MongoDB, they will out of the blue develop into unpredictable, gradual, and even trip. In some circumstances, processes that ran easily for years began working 25–30x slower after a like-for-like migration. Nearly all of the problems might be factored into the next broad classes:

• Excessive community round-trips between the applying and the database.
• Inefficient per-record operations changing set-based logic.
• Beneath-utilization of database bulk capabilities.
• Utility-layer computation overhead when reworking massive datasets.

For groups migrating complicated ETL-like processes, this wasn’t only a technical nuisance—it turned a blocker for modernization at scale.

The breakthrough: A batch job optimization framework

We designed an extensible, multi-purpose & resilient batch optimization framework purpose-built for high-volume, multi-collection operations in MongoDB. The framework focuses on minimising application-database friction whereas retaining the pliability of Java providers.

Key rules embody:

1. Bulk operations at scale: Leveraging MongoDB’s native “`bulkWrite“` (together with multi-collection bulk transactions in MongoDB 8) to course of 1000’s of operations in a single spherical journey.
2. Clever prefetching: Decreasing repeated lookups by pre-loading and caching reference knowledge in memory-friendly constructions.
3. Parallel processing: Partitioning workloads throughout threads or occasion processors (e.g., Disruptor sample) for CPU-bound and I/O-bound steps.
4. Configurable batch sizes: Dynamically tuning batch chunk sizes to steadiness reminiscence utilization, community payload measurement, and commit frequency.
5. Pluggable transformation modules: Modularized knowledge transformation logic that may be reused throughout a number of processes.

Technical structure

The framework adopts a layered and orchestrated method to batch job processing, the place every element has a definite duty within the end-to-end workflow. The diagram illustrates the circulation of a batch execution:

1. Set off (consumer / cron job): The batch course of begins when a consumer motion or a scheduled cron job triggers the Spring Boot controller.
2. Spring boot controller: The controller initiates the method by fetching the related data from the database. As soon as retrieved, it splits the data into batches for parallel execution.
3. Database: Acts because the supply of fact for enter knowledge and the vacation spot for processed outcomes. It helps each reads (to fetch data) and writes (to persist batch outcomes).
4. Executor framework: This layer is answerable for parallelizing workloads. It distributes batched data, manages concurrency, and invokes ETL duties effectively.
5. ETL course of: The ETL (Extract, Remodel, Load) logic is utilized to every batch. Knowledge is pre-fetched, reworked based on enterprise guidelines, after which loaded again into the database.
6. Completion & write-back: As soon as ETL operations are full, the executor framework coordinates database write operations and alerts the completion of the batch.

From bottleneck to benefit

The outcomes had been putting. Batch jobs that beforehand timed out at the moment are accomplished predictably inside outlined SLAs, and workloads that had initially run 25–30x slower after migration had been optimized to carry out on par with legacy RDBMSs and in a number of circumstances even ship 10–15x higher efficiency. What was as soon as a bottleneck turned a aggressive benefit, proving that batch processing on MongoDB can considerably outperform legacy PL/SQL when applied with the correct optimization framework.

Caveats and tuning ideas

Whereas the framework is adaptable, its efficiency is determined by workload traits and infrastructure limits:

• Batch measurement tuning: Too massive may cause reminiscence stress; too small will increase round-trips.
• Transaction boundaries: MongoDB transactions have limits (doc measurement, whole operations), plan batching accordingly.
• Thread pool sizing: Over-parallelization can overload the database or community.
• Index technique: Even with bulk writes, poor indexing may cause slowdowns.
• Prefetch scope: Stability reminiscence utilization towards lookup frequency.

In brief, it’s not one measurement matches all. Each workload is completely different, the info you course of, the foundations you apply, and the dimensions you run in any respect form how issues carry out. What we’ve seen although is that with the correct tuning, this framework can deal with scale reliably and take batch processing from being a ache level to one thing that truly offers you an edge.

In case you’re exploring learn how to modernize your individual workloads, this method is a stable place to begin. You may decide and select the elements that make sense in your setup, and adapt as you go.