7 ms·

> runs SQL in desired order Sure, one at a time. dbt builds a DAG of the SQL operations and can run transformations concurrently.

riku_iki 3y ago
and why would you need this? If the goal is to reduce e2e latency, then optimizing SQL is likely much more beneficial, since it can improve performance by NNN times, not just by N times.
- tomnipotent 3y ago
  > and why would you need this? So things finish considerably faster? > then optimizing SQL is likely much more beneficial This only gets you so far. Running transformations one-at-a-time when the server can do more is just holding you back. My last dbt project had over 300+ tables and running transformations serially would have taken an order of magnitude longer than running concurrently.
  - riku_iki 3y ago
    > Running transformations one-at-a-time when the server can do more is just holding you back in general, the end goal of such ETL optimization is to have all your CPU or IO saturated. But I imagine for 300 tables transformations it could be too much work.
    - tomnipotent 3y ago
      The goal of ETL is to transform data for practical applications, for which CPU and memory is there to be utilized. This particular dbt process was running against the second smallest Redshift instance, which barely noticed when the process kicked off every 10 minutes to run incremental updates. Would run for maybe 60-90 seconds, with only the occasional lock contention with long-running analyst queries that was easily mitigated.
      - riku_iki 3y ago
        > The goal of ETL is I said goal of "such ETL optimization", not ETL. Luckily for me it is an hour and 50 lines(likely less) of code to write such parallel executor with dependency tracking if need will arise.
        camgunz 3y ago
        I had the misfortune of having to reimplement dbt at work (we have a weirdo "don't directly connect to app DBs" rule so I had to add support for running arbitrary Python to fetch data from our microservices over gRPC), and yeah the dependency tracking, subsetting, testing, parallelism and such is super easy -- it's more like 1000 lines of Python/SQL for the basics, but yeah no sweat really. Where we ran into issues was: - Availability: the general model of dbt is DROP the model (OK by default they TRUNCATE but w/e) and rebuild it. But this means that model's unavailable (read: locked) during rebuilding. Further, you can't do that if any downstream view depends on your model. Even if you decide it's OK to drop the downstream view, rebuild all its upstream dependencies, then rebuild it, it's unavailable the whole time. Further, you probably don't have just one downstream view depending on these upstream models, so you've got to work out all the downstream views to DROP and then rebuild, which is like a secondary dependency tree to walk while walking the primary dependency tree. But, that mind-bending weirdness aside, you're essentially making your whole DB unavailable during a build. Sometimes this is OK, for us it really wasn't. - Incremental tables added a lot of complexity: rebuilding bigger tables that mostly don't change is a big waste of time and resources. But this is an entirely parallel code path which more or less doubled our code burden. We tried pretty hard to manage these, which ultimately was a big rabbit hole. We eventually settled on schema versioning where we'd build a whole new DB in a different schema, run tests on it, and promote it if it passed. This let us avoid both the availability and incremental ditches, and as a bonus we could instantly restore past schemas if we discovered issues (restoring DB backups is sloooooooow and lossy). But, I think the engineering lesson here was we either should have rescinded that weirdo app DB rule, or restricted our custom work to ELTing the data out of our apps and used dbt downstream of that.
- camgunz 3y ago
  Well there's 2 main reasons. The first is that you can update a subset of your pipeline, like say you have an internal dashboard for server stats and another external dashboard that's a product for customers, and you notice a problem w/ the server stats dashboard. You can have dbt just build the models that underlie the server stats dashboard and also exempt any model used by the external dashboard. The second is that you can effectively defer processing. dbt builds views by default (though it of course will also build tables, including ephemeral tables that are cleaned up after a build), which means your transforms don't take place until you query the data. If you can get away with it, this is what you want, and there are lots of ways to stay on views (caching, use BigQuery, etc). This means your pipeline at least has the potential of being "real time". But, overall we're talking about pipelines that have 100s or 1000s of models. You need some kind of system for that; a shell script isn't gonna cut it.
  - riku_iki 3y ago
    You obviously can have modularity with shell scripts: run_all_etl.sh build_internal_dashboard_etl.sh (called from run_all_etl.sh) build_external_dashboard_etl.sh (called from run_all_etl.sh)
    - itsoktocry 3y ago
      How many models are in "run all etl", and what if one fails? You can't see how issues can arise here, with what you describe? I mean, go ahead and write your SQL pipeline in bash. Many of us are using dbt because we see a lot of benefit, we aren't all morons.
      - riku_iki 3y ago
        > How many models are in "run all etl", and what if one fails? hundred tables. one fail -> whole pipeline fails to ensure e2e and overall consistency.
    - camgunz 3y ago
      No you misunderstand. If you have some kind of dependency tree that's like: A B C D 1 2 3 i ii Ai i-ii ...and you want to only rebuild Ai and anything that it might rely on, all you do is `dbt build +Ai`. If you want to rebuild B and update anything that relies on it, all you do is `dbt build B+` > You obviously can have modularity with shell scripts Just as kind of a meta point, I think you're assuming that people are introducing a lot of incidental complexity into this problem, but I don't think you're open to the possibility that there's a lot of essential complexity here. I'm talking about DAG tools and the features that hang off of them, and you're saying "hey there's new thing called shell scripting". That's not useful in this conversation. Maybe going forward, let's assume people know about Bash.

riku_iki 3y ago

and why would you need this? If the goal is to reduce e2e latency, then optimizing SQL is likely much more beneficial, since it can improve performance by NNN times, not just by N times.

tomnipotent 3y ago
> and why would you need this? So things finish considerably faster? > then optimizing SQL is likely much more beneficial This only gets you so far. Running transformations one-at-a-time when the server can do more is just holding you back. My last dbt project had over 300+ tables and running transformations serially would have taken an order of magnitude longer than running concurrently.
- riku_iki 3y ago
  > Running transformations one-at-a-time when the server can do more is just holding you back in general, the end goal of such ETL optimization is to have all your CPU or IO saturated. But I imagine for 300 tables transformations it could be too much work.
  - tomnipotent 3y ago
    The goal of ETL is to transform data for practical applications, for which CPU and memory is there to be utilized. This particular dbt process was running against the second smallest Redshift instance, which barely noticed when the process kicked off every 10 minutes to run incremental updates. Would run for maybe 60-90 seconds, with only the occasional lock contention with long-running analyst queries that was easily mitigated.
    - riku_iki 3y ago
      > The goal of ETL is I said goal of "such ETL optimization", not ETL. Luckily for me it is an hour and 50 lines(likely less) of code to write such parallel executor with dependency tracking if need will arise.
      - camgunz 3y ago
        I had the misfortune of having to reimplement dbt at work (we have a weirdo "don't directly connect to app DBs" rule so I had to add support for running arbitrary Python to fetch data from our microservices over gRPC), and yeah the dependency tracking, subsetting, testing, parallelism and such is super easy -- it's more like 1000 lines of Python/SQL for the basics, but yeah no sweat really. Where we ran into issues was: - Availability: the general model of dbt is DROP the model (OK by default they TRUNCATE but w/e) and rebuild it. But this means that model's unavailable (read: locked) during rebuilding. Further, you can't do that if any downstream view depends on your model. Even if you decide it's OK to drop the downstream view, rebuild all its upstream dependencies, then rebuild it, it's unavailable the whole time. Further, you probably don't have just one downstream view depending on these upstream models, so you've got to work out all the downstream views to DROP and then rebuild, which is like a secondary dependency tree to walk while walking the primary dependency tree. But, that mind-bending weirdness aside, you're essentially making your whole DB unavailable during a build. Sometimes this is OK, for us it really wasn't. - Incremental tables added a lot of complexity: rebuilding bigger tables that mostly don't change is a big waste of time and resources. But this is an entirely parallel code path which more or less doubled our code burden. We tried pretty hard to manage these, which ultimately was a big rabbit hole. We eventually settled on schema versioning where we'd build a whole new DB in a different schema, run tests on it, and promote it if it passed. This let us avoid both the availability and incremental ditches, and as a bonus we could instantly restore past schemas if we discovered issues (restoring DB backups is sloooooooow and lossy). But, I think the engineering lesson here was we either should have rescinded that weirdo app DB rule, or restricted our custom work to ELTing the data out of our apps and used dbt downstream of that.
camgunz 3y ago
Well there's 2 main reasons. The first is that you can update a subset of your pipeline, like say you have an internal dashboard for server stats and another external dashboard that's a product for customers, and you notice a problem w/ the server stats dashboard. You can have dbt just build the models that underlie the server stats dashboard and also exempt any model used by the external dashboard. The second is that you can effectively defer processing. dbt builds views by default (though it of course will also build tables, including ephemeral tables that are cleaned up after a build), which means your transforms don't take place until you query the data. If you can get away with it, this is what you want, and there are lots of ways to stay on views (caching, use BigQuery, etc). This means your pipeline at least has the potential of being "real time". But, overall we're talking about pipelines that have 100s or 1000s of models. You need some kind of system for that; a shell script isn't gonna cut it.
- riku_iki 3y ago
  You obviously can have modularity with shell scripts: run_all_etl.sh build_internal_dashboard_etl.sh (called from run_all_etl.sh) build_external_dashboard_etl.sh (called from run_all_etl.sh)
  - itsoktocry 3y ago
    How many models are in "run all etl", and what if one fails? You can't see how issues can arise here, with what you describe? I mean, go ahead and write your SQL pipeline in bash. Many of us are using dbt because we see a lot of benefit, we aren't all morons.
    - riku_iki 3y ago
      > How many models are in "run all etl", and what if one fails? hundred tables. one fail -> whole pipeline fails to ensure e2e and overall consistency.
  - camgunz 3y ago
    No you misunderstand. If you have some kind of dependency tree that's like: A B C D 1 2 3 i ii Ai i-ii ...and you want to only rebuild Ai and anything that it might rely on, all you do is `dbt build +Ai`. If you want to rebuild B and update anything that relies on it, all you do is `dbt build B+` > You obviously can have modularity with shell scripts Just as kind of a meta point, I think you're assuming that people are introducing a lot of incidental complexity into this problem, but I don't think you're open to the possibility that there's a lot of essential complexity here. I'm talking about DAG tools and the features that hang off of them, and you're saying "hey there's new thing called shell scripting". That's not useful in this conversation. Maybe going forward, let's assume people know about Bash.