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Back in 2023, we had an idea: build an analytics platform that treasurers could actually use without headaches. It needed to deliver real-time insights into key metrics like net cashflow, balances, and average transaction sizes, while being flexible enough to create dashboards tailored to every organization’s unique needs.

Our first attempt—fondly dubbed Naivety—was built on a single-threaded Go runtime that crunched data using basic computations. It worked... until it didn’t. As we started handling more data and onboarding more customers, the cracks showed. Scalability? Struggled. Latency? Painful. Reliability? Let’s just say there were a few late-night panic sessions.

This post is a deep dive into how we leveled up. We’ll walk through the evolution of our platform: from early attempts with a precompute layer to fully embracing ClickHouse for ultra-fast querying and real-time updates. It wasn’t all smooth sailing, but we came out with a system that can handle millions of transactions every minute and still feel lightning fast.

Our Vision and Key Objectives

We set out to create an analytics platform that could deliver:

1. Real-time analytics and insights as fast as the data coming in.
2. Customizable dashboards to meet the unique reporting needs of each organization
3. Scalable data processing to deal with upto a million transactions being ingested every minute.

Analytics vBasic

Our initial implementation, which we affectionately call "Naivety," was on a Go runtime. This module aggregated data using mathematical primitives and single threaded evaluations.


To achieve the above objectives, we developed a strategy centered around two core components: an Analytics Precompute Layer and a Widget Layer

Precompute layer

This layer was designed to process every interesting event in our system, including when a new transaction gets ingested, a transaction is tagged and balances are updated from the banks.


The precompute layer's job was to stay current with all system events and perform necessary aggregations and computations.

func ComputeNetCashFlow(accountId string, date time.Time) (float64, error) {
    
    // Fetch transactions for the account and date
    transactions := GetTransactions(accountId, date)

    // Calculate net cash flow
    netCashFlow := 0.0
    for _, txn := range transactions {
        if txn.Type == "credit" {
            netCashFlow += txn.Amount
        } else if txn.Type == "debit" {
            netCashFlow -= txn.Amount
        }
    }

    return netCashFlow, nil
}

Widgets layer

We decoupled this layer from our analytics layer. Its sole responsibility was to query metrics from the precomputed analytics and transform them into a data format that our front-end could understand and display for each "component" as shown below.

func ComputeNetCashFlow(accountId string, date time.Time) (float64, error) {
    
    // Fetch transactions for the account and date
    transactions := GetTransactions(accountId, date)

    // Calculate net cash flow
    netCashFlow := 0.0
    for _, txn := range transactions {
        if txn.Type == "credit" {
            netCashFlow += txn.Amount
        } else if txn.Type == "debit" {
            netCashFlow -= txn.Amount
        }
    }

    return netCashFlow, nil
}

What it looked like?

Challenges with v1

While our initial approach seemed logical, we soon encountered several issues:

1. Scalability: Each event in our system triggered a recompute and had to calculate several metrics. This proved unsustainable for a Go Runtime.
2. High Latency: The system struggled to provide real-time insights due to processing delays as the number of customers onboarded increased.
3. Reliability: The complex event processing sometimes led to inconsistent or unreliable results due to race conditions and edge cases.

Analytics v2

Recognizing the limitations of our initial implementation, we identified key areas that needed improvement:

1. Handling numerous events with quick insertion - efficient recomputes of aggregates on each interesting event.
2. Precomputed aggregates for faster querying
3. Very low latency queries.
4. Near real-time updates

To address these challenges, we explored several OLAP solutions.

ClickHouse: Our chosen solution

ClickHouse is an OLAP database, very good at insertions, low-latency queries and supports materialized views to precompute aggregations.

Materialized Views

Materialized Views in ClickHouse allow us to precompute and store aggregated data efficiently. This feature is crucial for our real-time analytics needs:

• Automatic updates: When new data is inserted into the source table, ClickHouse automatically updates the materialized view with updated metrics in very low latency.
• Query optimization: Instead of computing aggregates on-the-fly, we can query pre-aggregated data, significantly reducing query time.
• Flexibility: We can create multiple materialized views for different aggregation levels, allowing us to optimize for various query patterns.

Engines

ClickHouse provides various table engines optimized for different use cases, allowing us to fine-tune our data storage and query performance:

• MergeTree: These engines form the backbone of our data storage, offering excellent insert and query performance for large datasets.
• ReplacingMergeTree: This engine helps us handle deduplication easily. Especially useful for our updates to existing transactions (like when a transaction was tagged).
• SummingMergeTree: Metrics like net cashflow is a sum of all transaction amounts. MVs defined with this engine are very optimized for such computations.

Operational Database and OLTP Scenarios

Our operational database, Google Cloud Spanner, continued to serve all of our business needs, especially for OLTP scenarios. Spanner's strong consistency model and global distribution capabilities made it ideal for handling our day-to-day transactional workloads.

Data Migration to ClickHouse

To leverage ClickHouse's analytical prowess, we needed to move data from Spanner to ClickHouse. We implemented this using a Change Data Capture (CDC) approach:

1. CDC Process: We set up a CDC pipeline to capture changes in our Spanner database in real-time.
2. PubSub as Message Queue: These changes were then published to Google Cloud PubSub, acting as a reliable message queue.
3. Data Ingestion: From PubSub, the data was streamed into our ClickHouse instance, ensuring near real-time data synchronization.

This approach allowed us to maintain an up-to-date copy of our operational data in ClickHouse, ready for complex analytical queries.

Challenges with ClickHouse

However, this approach came with its own set of challenges, primarily due to a limitation in ClickHouse. An MV can be good at only one thing - deduplication (ReplacingMergeTree) or aggregation (SummingMergeTree), but not both.

In our scenario, updates on a row would trigger a new entry in our CDC and a corresponding insertion into ClickHouse. This would have to go and replace the old r0w with the "new value".

Addressing this challenge: Lambda architecture

While ClickHouse proved to be an excellent solution for our analytics needs, implementing it within our existing infrastructure presented its own set of challenges. We adopted a Lambda architecture to bridge the gap between our operational needs and analytical capabilities. Here's a deeper look at our implementation.

We went with the Lambda Architecture because it gave us the best of both worlds: accuracy and speed. For real-time analytics, we needed a system that could process incoming events instantly while still providing consistent and reliable historical data.

The streaming layer ensures that our analytics stay as real-time as possible, handling millions of transactions as they come in. Meanwhile, the batch layer serves as the source of truth, running comprehensive computations on historical data to maintain accuracy and consistency.

By combining these two layers, we could deliver low-latency insights without compromising on data integrity—perfect for treasurers who need to make decisions backed by solid analytics. It wasn’t just about speed; it was about speed and accuracy of the values.

1. Ingestion
   1. We would continue to ingest data into our ReplacingMergeTree MVs which deduplicated data.
2. Batch Processing Layer:
   • We introduced a batch processing layer that runs at regular intervals (hourly).
   • This layer took data from these RMTs, ran complex aggregations on the ingested data (SUM, MAX, AVG etc.)
   • The results are stored in separate ClickHouse tables (MergeTree) optimized for quick reads.
3. Real-time Processing Layer:
   • For real-time insights, we use data from both these batched tables + data coming in after the last completed batch job and do real-time aggregations on that.
   • Then we combine both of those to display the final aggregations.

In this way we eliminated the need for aggregations

What did our pipeline look like finally?

Our pipeline is complex, but necessary for our use case.

The Road Ahead

Our journey from a naive implementation to a specialized, ClickHouse-based solution has significantly improved our analytics capabilities. We now have a robust, scalable, and real-time analytics platform that can handle the diverse needs of our users.

But our work isn't done. We continue to refine and optimize our analytics module. We're working on something that makes this module more customizable and implementing several AI features like prompt-based responses etc.

If you're interested in solving really difficult technical challenges - we're hiring. Please reach out to us at [email protected] or apply here.


Written by Giridhar Balachandran, Software Engineer, Zamp.