Octane Achieves <200ms Response, Scales to 100s of Terabytes, and Lowers TCO by 25% with SingleStore on Google Cloud
10s to 100s of millions of measurements per day
10s-100s
In the dataset
100s of TBs
Response time for real-time analytics
<200ms
Rows of time series data ingested/day
1M+
TCO with SingleStore
-25%
Octane, a unit of multinational financial services leader Stripe, is a leading usage-based billing platform built for software-as-service (SaaS) product and finance teams to support evolving monetization strategies. The platform provides the flexibility to price and bill based on any metric powered by real-time data, with simple integration to accounting tools and payment providers.
Octane lets companies free their pricing strategies from their engineering backlog
Challenges/Goals
Traditional SaaS billing software requires users to aggregate all the data on their side and then figure out pricing at the end of the month. It requires a lot of maintenance and infrastructure to support, and doesn’t allow SaaS companies to explore more dynamic monetization strategies. SaaS companies don’t want to pay flat fees, and Octane wants to help them align revenue with their customer’s value. As their customer drives more value from the product, the company can charge them more for the added value they are providing.
With deep backgrounds in building SaaS products at scale, Octane knows the importance of pricing flexibility, billing simplicity, and getting the right tools to the right stakeholders. When SaaS companies switch to usage-based billing, they can unlock a good boost in revenue and increase their net revenue retention.
Octane only requires a one-time setup process for engineers to integrate data into the system. They don’t have to continually check on or maintain it. The platform has real-time systems that ingest user data and provide continuously updated revenue dashboards.
The database Octane was using — Timescale, a time series database built on PostgreSQL — was unable to scale up to meet user demand. Queries were very slow, especially on data that spanned across time, resulting in an analytics experience that didn’t meet business user expectations. The real-time revenue dashboards becoming slow was a major issue, especially for users who are regularly checking these analytics.
To try to speed up performance, the Octane team had to create many indices on top of the database. However, this approach came with several drawbacks. Writes became slow because committed writes had to be written to every single index. Data storage also became bloated, because that data was copied in multiple indices. The team was running huge clusters with a lot of storage for data that wasn’t really that large, storing 10x the amount of data it needed because of all the indices it had on top of the database
Another challenge related to the database’s slow performance involved Octane’s real-time alert system, which triggers when users cross certain usage thresholds. Potential cross-selling and upselling opportunities were lost due to the lengthy delays. Horizontal scalability was also an issue. The team couldn’t scale horizontally and use a distributed database to help scale queries faster.
Technology Requirements
The existing combination of the time series database and Kafka for message-passing to achieve high throughput was not keeping up with Octane’s growth. The team started to look at alternative database solutions to better support the platform.
The ideal solution would have:
- Extremely high-throughput reads and writes
- Real-time processing on 10,000 measurements per second
- Online analytical processing (OLAP), online transaction processing (OLTP), hybrid transaction / analytical processing (HTAP) capabilities in a unified database
- SQL support, especially around aggregating queries on time series data
- The ability to meet compliance requirements for sensitive billing data, offering consistent, secure, and transactional reliability
The team wanted the database to be SQL and transactional because Octane operates on sensitive revenue data. At the same time, it needed to be extremely performant for analytical workloads because the team wanted to be able to do analytical queries without sacrificing transactional performanc.
Why SingleStore
Octane considered several other databases in its search for a solution that could keep up with the pace of business.
Octane did some experimentation with Snowflake, but taking on low-value work maintaining both OLTP and OLAP data sources was a dealbreaker. The team also looked at streaming database options like Materialize, but those solutions fell short on the predictable, reliable performance Octane needed because they did not support ANSI SQL. Then Octane discovered SingleStore, a real-time, unified, distributed SQL database. SingleStore offered hybrid workloads, transactional processing, analytical data, and high throughput writes combined with horizontal scalability and cost-efficiency. With SingleStore, the team knows it can infinitely scale without having to worry about overloading the system.
Solution
Octane opted for SingleStore Helios, the fully-managed, on-demand cloud database service, running on Google Cloud, to power data-intensive applications. The team uses two SingleStore clusters; one is a production cluster and the other a development cluster. SingleStore is the core data store, powering all analytics dashboards and ETL pipeline reads and writes. Octane is using SingleStore for both transactional and analytical workloads throughout the system. Other SingleStore features Octane uses include:
The primary ingestion pipelines are Kafka-based, used for message passing, and that data is then written to SingleStore. Python, Go, SQL Alchemy, and GORM make up the rest of Octane’s technology stack and talk directly with the database. The team does not use any off-the-shelf business intelligence tools. Instead, it writes queries on top of SingleStore Helios. It receives millions of rows a day of time series data, ingests it into SingleStore, runs real-time analytics on it, then also runs the transactional billing process.
While Octane’s queries do not have many joins, they are complex in different ways. Octane does have a lot of group-bys, aggregations in group-bys, and time series aggregate functions like latest max-min, first-last, in several group-by clauses.
Outcomes
Since choosing SingleStore Helios as its primary unified data store, Octane is experiencing these and other benefits:
Scalability to Support Future Growth and 100s of TBs of Data at 25% Lower TCO
Octane is no longer constrained by the horizontal scaling limitations it suffered with its original database solution. While the platform’s data currently sits in the one-to-10 terabyte (TB) range, the team anticipates stabilizing at 100s of TB of data. While Octane is operating at a higher scale than it did with its previous solution, it’s also saving, lowering its total cost of ownership (TCO) by 25%. The company can now onboard customers that have very high real-time throughput requirements. It has customers sending thousands of requests per second. Before it adopted SingleStore, it had to be careful with onboarding those customers, but no longer.
Achieving High Transactional Writes and Analytical Reads on 10,000+ Measurements per Second
SingleStore Helios’s unified data engine for transactional and analytical workloads powers Octane’s fast, real-time analytics platform. Sensitive billing and revenue data are consistent, secure, and transactional for reliability. Meantime, real-time usage data from Octane customers can come into the system at very high throughput with 10,000 measurements per second.
Helping Business Users Act Quickly on Their SaaS Revenue Data through Real-time Dashboards with <200ms Response Times
In the analytics dashboard, Octane looks for responses in a few seconds; with SingleStore it is receiving responses in less than 200 millseconds. This enables business users who want to check in on their revenue dashboards to have real-time access. Octane ingests their data and continuously updates their revenue dashboards, giving them what they need to do their jobs.
Octane’s scalable platform ingests 10s to 100s of millions of measurements per day
Continuing to Break New Ground in Real-Time SaaS Billing
Octane plans to help users take a deep dive into price experimentation with back testing, forecasting, price matching to different customers and cohorts, and other ways of helping SaaS companies get the most value out of usage-based billing. With SingleStore it now has a reliable, fast database that’s necessary to do modeling and algorithmic analysis on that data.
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