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Performance

TigerBeetle provides more performance than a general-purpose relational database such as MySQL or an in-memory database such as Redis:

  • TigerBeetle uses small, simple fixed-size data structures (accounts and transfers) and a tightly scoped domain.

  • TigerBeetle uses multiple Log-Structured Merge (LSM) Trees for storing objects and indices. This data structure is highly optimized for write-heavy workloads like Online Transaction Processing (OLTP). TigerBeetle squeezes even more performance out of LSM trees by using multiple trees, each storing separate types of homogeneous data.

  • TigerBeetle performs all balance tracking logic in the database. This is a paradigm shift where we move the code once to the data, not the data back and forth to the code in the critical path. This eliminates the need for complex caching logic outside the database. The “Accounting” business logic is built into TigerBeetle so that you can keep your application layer simple and completely stateless.

  • TigerBeetle supports batching by design. You can batch all the transfer prepares or commits that you receive in a fixed 10ms window (or in a dynamic 1ms through 10ms window according to load) and then send them all in a single network request to the database. This enables low-overhead networking, large sequential disk write patterns and amortized fsync and consensus across hundreds and thousands of transfers.

Everything is a batch. It's your choice whether a batch contains 100 transfers or 10,000 transfers but our measurements show that latency is less where batch sizes are larger, thanks to Little's Law (e.g. 50ms for a batch of a hundred transfers vs 20ms for a batch of ten thousand transfers). TigerBeetle is able to amortize the cost of I/O to achieve lower latency, even for fairly large batch sizes, by eliminating the cost of queueing delay incurred by small batches.

  • If your system is not under load, TigerBeetle also optimizes the latency of small batches. After copying from the kernel's TCP receive buffer (TigerBeetle does not do user-space TCP), TigerBeetle does zero-copy Direct I/O from network protocol to disk, and then to state machine and back, to reduce memory pressure and L1-L3 cache pollution.

  • TigerBeetle uses io_uring for zero-syscall networking and storage I/O. The cost of a syscall in terms of context switches adds up quickly for a few thousand transfers. (You can read about the security of using io_uring here.)

  • TigerBeetle does zero-deserialization by using fixed-size data structures that are optimized for cache line alignment to minimize L1-L3 cache misses.

  • TigerBeetle takes advantage of Heidi Howard's Flexible Quorums to reduce the cost of synchronous replication to one (or two) remote replicas at most (in addition to the leader) with asynchronous replication between the remaining followers. This improves write availability without sacrificing strict serializability or durability. This also reduces server deployment cost by as much as 20% because a 4-node cluster with Flexible Quorums can now provide the same f=2 guarantee for the replication quorum as a 5-node cluster.

"The major availability breakdowns and performance anomalies we see in cloud environments tend to be caused by subtle underlying faults, i.e. gray failure (slowly failing hardware) rather than fail-stop failure."

  • TigerBeetle routes around transient gray failure latency spikes. For example, if a disk write that typically takes 4ms starts taking 4 seconds because the disk is slowly failing, TigerBeetle will use cluster redundancy to mask the gray failure automatically without the user seeing any 4-second latency spike. This is a relatively new performance technique in the literature known as "tail tolerance".

Single-Core By Design​

TigerBeetle uses a single core by design and uses a single leader node to process events. Adding more nodes can therefore increase reliability but not throughput.

For a high-performance database, this may seem like an unusual choice. However, sharding in financial databases is notoriously difficult and contention issues often negate the would-be benefits. Specifically, a small number of hot accounts are often involved in a large proportion of the transactions so the shards responsible for those accounts become bottlenecks.

For more details on when single-threaded implementations of algorithms outperform multi-threaded implementations, see "Scalability! But at what COST?.