5 ms·

> I can't speak to others†, but for our use-case, we're not using NVMe as "persistent storage but faster and smaller" but rather as "main memory but larger and

> I can't speak to others†, but for our use-case, we're not using NVMe as "persistent storage but faster and smaller" but rather as "main memory but larger and slower". > Our data isn't canonically on our nodes' NVMe pools, any more than it's canonically in the the nodes' local memory. The canonical representation of our (PGSQL data warehouse) data is its WAL log segments, shipped to/from object storage. I appreciate this insight -- one of the features that drew me to KeyDB[0] was their FLASH feature, with drives being as fast as they are these days it feels like having tiered cache with DRAM + NVMe disk isn't so bad! Great to see others think this way as well. Redis is one thing but treating pg this way is somewhat interesting -- Do you also have the usual hot standby setup with synchronous replication just in case of a machine going down before the latest WAL segment is shipped out? > † I would note that "large ephemeral working-set backing store" is seemingly largely considered by IaaS providers to be the main use-case for having NVMe attached to compute. The IaaS providers that support NVMe (AWS, GCP) only support it as instanced or scratch (i.e. ephemeral) storage, that doesn't survive node shutdown. These are strictly "L4 memory", not stores-of-record. Reasonable, thanks for explicitly noting this. I think the write characteristics (and technically the read as this article shows) of NVMe do make it attractive but don't preclude using it as the store of record medium but what you said definitely syncs up with the other configurations I've seen and the offerings at AWS (I haven't really looked at GCP node types). [0]: https://github.com/EQ-Alpha/KeyDB/wiki/Legacy-FLASH-Storage https://github.com/EQ-Alpha/KeyDB/wiki/Legacy-FLASH-Storage

derefr 5y ago
> Do you also have the usual hot standby setup with synchronous replication just in case of a machine going down before the latest WAL segment is shipped out? We don't have to — the data in our data warehouse is a rearrangement/normalization/collation of origin data from public sources. If our DB cluster dies, we stand up a new one from a backup, let it catch up from WAL segments, and then our ETL pipeline will automatically re-bind to it, discover the newest data it has available inside it, and begin ETLing to it. From our ETL ingestion-agent's perspective, there's no difference between "loading to a DB freshly restored from backup because it lost 10 minutes of updates" and "loading to a DB that is/was up-to-date because we just received some new origin data." (Our situation of our primary-source data being easily and reliably re-fetched is pretty unique to our vertical; but you can put pretty much any system into the same shape by first writing your primary-source data to an event store / durable unbounded-size MQ [e.g. Kafka] as you receive it; and then ETLing it into your DB from there. Forget DB disaster recovery; just keep the MQ data safe, and you'll always be able to replay the process that translates an MQ event-stream into a DB state.) We do have hot standbys, but they're async (using wal-g — master writes WAL segments to object store; replicas immediately fetch WAL segments from object store, as quickly as they can manage.) They're for query load scaling, not disaster recovery.
- hardwaresofton 5y ago
  Appreciate the detailed description of what works for your warehousing solution! > (Our situation of our primary-source data being easily and reliably re-fetched is pretty unique to our vertical; but you can put pretty much any system into the same shape by first writing your primary-source data to an event store / durable unbounded-size MQ [e.g. Kafka] as you receive it; and then ETLing it into your DB from there. Forget DB disaster recovery; just keep the MQ data safe, and you'll always be able to replay the process that translates an MQ event-stream into a DB state.) I'm familiar with this almost-event-sourcing model. AFAIK is what banks and financial firms do (at scales I can't imagine and with technology they'll never open source) and is the only way to do rock-solid lose-nothing architecture that is relatively easy to reason about and flexible. I have no need to do this but Debezium w/ NATS + Jetstream/Liftbridge would be my pick for this, since I'm a sucker for slightly newer tooling that boasts at being "cloud native" (i.e. easy for me to administer). AFAIK the boring choice might be Debezium + Kafka with some RAIDx drives below it all just in case. > We do have hot standbys, but they're async (using wal-g — master writes WAL segments to object store; replicas immediately fetch WAL segments from object store, as quickly as they can manage.) They're for query load scaling, not disaster recovery. Read only replicas for load scaling is pretty common, was surprised to hear wal-g though -- pg has an absolutely dizzying array of replication options[0], and I don't think I landed on wal-g as the "best" choice for me (with a heavily application-centric viewpoint) and this gives me reasonf or some pause. I think I mostly had it down to barman or pgbackrest. The object-store-capable options always caught my eye (and what put backrest over the top for me IIRC), will go back and revisit. Does covalent maintain an engineering blog? because sounds like ya'll are doing stuff I'd love to read about and cargo cult^H^H critically consider. Weirdly enough I've just been down a bit of a rabbit hole on the data engineering world and have been thoroughly impressed by dbt[1]. The concept is simple, but combined with a powerful database like pg it holds a lot of promise in my mind. I'm mostly excited by the idea of exposing the power of postgres and it's many extensions and features to a SQL-first view of the world that dbt encourages, and building some sort of "data platform" that is nothing more than enshrining and exposing the best ways to do things with that stack. Is dbt or a similar tool a part of your stack in any way? Listening to people who sort of... distribute hype around data engineering, you'd think Snowflake is the next greatest thing since sliced bread, but the pattern of debezium + dbt + a big ol' well-configured and replicated postgres cluster feels like more than enough. I'm aware of course that Snowflake is really just a management layer (essentially, they're an Redshift-aaS provider for any cloud) but it feels like that gets lost in discussion. Sounds like you all have a well considered data pipeline (so the snowflake part is done for you), wondering what the other links in the chain look like. Any Metabase or Superset at the end of the pipeline? [EDIT] Want to note the recent presentation from CERN on the topic of replication that most recently influenced me[2] (skip to the last 2 slides if you want the TL;DR) [0]: https://wiki.postgresql.org/wiki/Binary_Replication_Tools https://wiki.postgresql.org/wiki/Binary_Replication_Tools [1]: https://docs.getdbt.com https://docs.getdbt.com [2]: https://www.slideshare.net/AnastasiaLubennikova/advanced-backup-methods-postgrescern https://www.slideshare.net/AnastasiaLubennikova/advanced-bac...

derefr 5y ago

> Do you also have the usual hot standby setup with synchronous replication just in case of a machine going down before the latest WAL segment is shipped out? We don't have to — the data in our data warehouse is a rearrangement/normalization/collation of origin data from public sources. If our DB cluster dies, we stand up a new one from a backup, let it catch up from WAL segments, and then our ETL pipeline will automatically re-bind to it, discover the newest data it has available inside it, and begin ETLing to it. From our ETL ingestion-agent's perspective, there's no difference between "loading to a DB freshly restored from backup because it lost 10 minutes of updates" and "loading to a DB that is/was up-to-date because we just received some new origin data." (Our situation of our primary-source data being easily and reliably re-fetched is pretty unique to our vertical; but you can put pretty much any system into the same shape by first writing your primary-source data to an event store / durable unbounded-size MQ [e.g. Kafka] as you receive it; and then ETLing it into your DB from there. Forget DB disaster recovery; just keep the MQ data safe, and you'll always be able to replay the process that translates an MQ event-stream into a DB state.) We do have hot standbys, but they're async (using wal-g — master writes WAL segments to object store; replicas immediately fetch WAL segments from object store, as quickly as they can manage.) They're for query load scaling, not disaster recovery.

hardwaresofton 5y ago
Appreciate the detailed description of what works for your warehousing solution! > (Our situation of our primary-source data being easily and reliably re-fetched is pretty unique to our vertical; but you can put pretty much any system into the same shape by first writing your primary-source data to an event store / durable unbounded-size MQ [e.g. Kafka] as you receive it; and then ETLing it into your DB from there. Forget DB disaster recovery; just keep the MQ data safe, and you'll always be able to replay the process that translates an MQ event-stream into a DB state.) I'm familiar with this almost-event-sourcing model. AFAIK is what banks and financial firms do (at scales I can't imagine and with technology they'll never open source) and is the only way to do rock-solid lose-nothing architecture that is relatively easy to reason about and flexible. I have no need to do this but Debezium w/ NATS + Jetstream/Liftbridge would be my pick for this, since I'm a sucker for slightly newer tooling that boasts at being "cloud native" (i.e. easy for me to administer). AFAIK the boring choice might be Debezium + Kafka with some RAIDx drives below it all just in case. > We do have hot standbys, but they're async (using wal-g — master writes WAL segments to object store; replicas immediately fetch WAL segments from object store, as quickly as they can manage.) They're for query load scaling, not disaster recovery. Read only replicas for load scaling is pretty common, was surprised to hear wal-g though -- pg has an absolutely dizzying array of replication options[0], and I don't think I landed on wal-g as the "best" choice for me (with a heavily application-centric viewpoint) and this gives me reasonf or some pause. I think I mostly had it down to barman or pgbackrest. The object-store-capable options always caught my eye (and what put backrest over the top for me IIRC), will go back and revisit. Does covalent maintain an engineering blog? because sounds like ya'll are doing stuff I'd love to read about and cargo cult^H^H critically consider. Weirdly enough I've just been down a bit of a rabbit hole on the data engineering world and have been thoroughly impressed by dbt[1]. The concept is simple, but combined with a powerful database like pg it holds a lot of promise in my mind. I'm mostly excited by the idea of exposing the power of postgres and it's many extensions and features to a SQL-first view of the world that dbt encourages, and building some sort of "data platform" that is nothing more than enshrining and exposing the best ways to do things with that stack. Is dbt or a similar tool a part of your stack in any way? Listening to people who sort of... distribute hype around data engineering, you'd think Snowflake is the next greatest thing since sliced bread, but the pattern of debezium + dbt + a big ol' well-configured and replicated postgres cluster feels like more than enough. I'm aware of course that Snowflake is really just a management layer (essentially, they're an Redshift-aaS provider for any cloud) but it feels like that gets lost in discussion. Sounds like you all have a well considered data pipeline (so the snowflake part is done for you), wondering what the other links in the chain look like. Any Metabase or Superset at the end of the pipeline? [EDIT] Want to note the recent presentation from CERN on the topic of replication that most recently influenced me[2] (skip to the last 2 slides if you want the TL;DR) [0]: https://wiki.postgresql.org/wiki/Binary_Replication_Tools https://wiki.postgresql.org/wiki/Binary_Replication_Tools [1]: https://docs.getdbt.com https://docs.getdbt.com [2]: https://www.slideshare.net/AnastasiaLubennikova/advanced-backup-methods-postgrescern https://www.slideshare.net/AnastasiaLubennikova/advanced-bac...