Comment by kelseydh

3 months ago

We didn't observe any automatic batching when testing Tigerbeetle with their Go client. I think we initiated a new Go client for every new transaction when benchmarking, which is typically how one uses such a client in app code. This follows with our other complaint: it handles so little you will have to roll a lot of custom logic around it to batch realtime transactions quickly.

I'm a bit worried you think instantiating a new client for every request is common practice. If you did that to Postgres or MySQL clients, you would also have degradation in performance.

PHP has created mysqli or PDO to deal with this specifically because of the known issues of it being expensive to recreate client connects per request

  • Ok your comment made me double check our benchmarking script in Go. Can confirm we didn't instantiate a new client with each request.

    For transparency here's the full Golang benchmarking code and our results if you want to replicate it: https://gist.github.com/KelseyDH/c5cec31519f4420e195114dc9c8...

    We shared the code with the Tigerbeetle team (who were very nice and responsive btw), and they didn't raise any issues with the script we wrote of their Tigerbeetle client. They did have many comments about the real-world performance of PostgreSQL in comparison, which is fair.

    • Thanks for the code and clarification. I'm surprised the TB team didn't pick it up, but your individual transfer test is a pretty poor representation. All you are testing there is how many batches you can complete per second, giving no time for the actual client to batch the transfers. This is because when you call createTransfer in GO, that will synchronously block.

      For example, it is as if you created an HTTP server that only allows one concurrent request. Or having a queue where only 1 worker will ever do work. Is that your workload? Because I'm not sure I know of many workloads that are completely sync with only 1 worker.

      To get a better representation for individual_transfers, I would use a waitgroup

        var wg sync.WaitGroup
        var mu sync.Mutex
        completedCount := 0
      
        for i := 0; i < len(transfers); i++ {
          wg.Add(1)
          go func(index int, transfer Transfer) {
           defer wg.Done()
      
           res, _ := client.CreateTransfers([]Transfer{transfer})
           for _, err := range res {
            if err.Result != 0 {
             log.Printf("Error creating transfer %d: %s", err.Index, err.Result)
            }
           }
      
           mu.Lock()
           completedCount++
           if completedCount%100 == 0 {
            fmt.Printf("%d\n", completedCount)
           }
           mu.Unlock()
          }(i, transfers[i])
         }
      
        wg.Wait()
        fmt.Printf("All %d transfers completed\n", len(transfers))
      

      This will actually allow the client to batch the request internally and be more representative of the workloads you would get. Note, the above is not the same as doing the batching manually yourself. You could call createTransfer concurrently the client in multiple call sites. That would still auto batch them

    • Appreciate your kind words, Kelsey!

      I searched the recent history of our community Slack but it seems it may have been an older conversation.

      We typically do code review work only for our customers so I’m not sure if there was some misunderstanding.

      Perhaps the assumption that because we didn’t say anything when you pasted the code, therefore we must have reviewed the code?

      Per my other comment, your benchmarking environment is also a factor. For example, were you running on EBS?

      These are all things that our team would typically work with you on to accelerate you, so that you get it right the first time!

      2 replies →

Interesting, I thought I had heard that this is automatically done, but I guess it's only through concurrent tasks/threads. It is still necessary to batch in application code.

https://docs.tigerbeetle.com/coding/clients/go/#batching

But nonetheless, it seems weird to test it with singular queries, because Tigerbeetle's whole point is shoving 8,189 items into the DB as fast as possible. So if you populate that buffer with only one item your're throwing away all that space and efficiency.

  • We certainly are losing that efficiency, but this is typically how real-time transactions work. You write real-time endpoints to send off transactions as they come in. Needing to roll more than that is a major introduction of complexity.

    We concluded where Tigerbeetle really shines is if you're a large entity like a central bank or corporation sending massive transaction files between entities. Tigerbeetle is amazing for moving large numbers of batch transactions at once.

    We found other quirks with Tigerbeetle that made it difficult as a drop-in replacement for handling transactions in PostgreSQL. E.g. Tigerbeetle's primary ID key isn't UUIDv7 or ULID, it's a custom id they engineered for performance. The max metadata you can save on a transaction is a 128-bit unsigned integer on the user_data_128 field. While this lets them achieve lightning fast batch transaction processing benchmarks, the database allows for the saving of so little metadata you risk getting bottlenecked by all the attributes you'll need to wrap around the transaction in PostgreSQL to make it work in a real application.

    • > you risk getting bottlenecked by all the attributes you'll need to wrap around the transaction in PostgreSQL to make it work in a real application.

      The performance killer is contention, not writing any associated KV data—KV stores scale well!

      But you do need to preserve a clean separation of concerns in your architecture. Strings in your general-purpose DBMS as "system of reference" (control plane). Integers in your transaction processing DBMS as "system of record" (data plane).

      Dominik Tornow wrote a great blog post on how to get this right (and let us know if our team can accelerate you on this!):

      https://tigerbeetle.com/blog/2025-11-06-the-write-last-read-...

> We didn't observe any automatic batching when testing Tigerbeetle with their Go client.

This is not accurate. All TigerBeetle's clients also auto batch under the hood, which you can verify from the docs [0] and the source [1], provided your application has at least some concurrency.

> I think we initiated a new Go client for every new transaction when benchmarking

The docs are careful to warn that you shouldn't be throwing away your client like this after each request:

  The TigerBeetle client should be shared across threads (or tasks, depending on your paradigm), since it automatically groups together batches of small sizes into one request. Since TigerBeetle clients can have at most one in-flight request, the client accumulates smaller batches together while waiting for a reply to the last request.

Again, I would double check that your architecture is not accidentally serializing everything. You should be running multiple gateways and they should each be able to handle concurrent user requests. The gold standard to aim for here is a stateless layer of API servers around TigerBeetle, and then you should be able to push pretty good load.

[0] https://docs.tigerbeetle.com/coding/requests/#automatic-batc...

[1] The core batching logic powering all language clients: https://github.com/tigerbeetle/tigerbeetle/blob/main/src/cli...