Comment by whalesalad

4 months ago

I've been absolutely waist deep in a bitemporal system on top of PostgreSQL using tstzrange fields. We manage an enormous portfolio of hundreds of thousands of domain names. Every time our core db is modified, before/after states are emitted to a change table. We've been doing this since 2022. Those changes get lightly transformed via trigger into a time travel record, with the valid from/to range and a gist index to make asking questions about the state of the world at a particular point in time easy. For perspective our change table has 90M rows.

All of it works quite well and is decently performant. We can ask questions like, how many domains did we own on March 13th, 2024? Or look at the entire lifecycle of a domains ownership (owned, released, re-acquired, transfered, etc).

The big challenge and core issue we discovered though is that our data sucks. QAing this new capability has been a moving target. Tons of mistakes over time that were partially undone or manually undone without proper audit trail. Ghost records. Rapid changes by our bulk editor tool a->b->a->b that need to get squashed into just a->b. The schema of our database has evolved over time, too, which has made this tough to view a consistent representation of things even if the fields storing that data were renamed. When the system was first introduced, we had ~5 columns to track. Now we have over 30.

Suffice to say if I were to do things over again, I would implement a much better change tracking system that bakes in tools to clean/erase/undo/soft-delete/hard-delete mistakes so that future me (now) wouldn't have so many edge cases to deal with in this time traveling system. I'd also like to just make the change tracking capable of time travel itself, versus building that as a bolt-on side table that tracks and works from the change table. Transitioning to an EAV (entity-attr-value) approach is on my spike list, too. Makes it easier to just reduce (key,val) tuples down into an up to date representation versus looking at diffs of before/after.

Really interesting stuff. I learned a lot about this from Clojure/Datomic and think its quite neat that so many Clojurists are interested in and tackling this problem. As the author notes in this post, XTDB is another one.

The XTDB people did a bunch of interviews with people doing stuff in the temporal data systems universe.

Several are out on YouTube. One with Richard Snodgrass is yet to be published... waiting for that eagerly!

ref. topics and links to videos: https://github.com/orgs/xtdb/discussions/4419

  "Decades in the making: Bitemporality in SQL, with Rob Squire"

  "As-Of joins and database architecture, with Arjun Narayan (co-founded Materialize)"

  "Building an identity service on XTDB, with Andrew Maddock (Foundry OS)"  

  "Solving FinTech pains with Suresh Nageswaran (Senior Director, Grid Dynamics)"

  "A meeting with the one and only Richard T. Snodgrass."

  "Building a payments integration system on XTDB, with Edward Hughes"

What I’ve found works well, going along with the author’s “everything is a log”, is append only tables in PG with date ranges on them.

So you have a pet table with an ID, birth date, type, name, whatever, and ‘valid_range’.

That last column is a date_range column. Combined with the ID it serves as a unique key for the table. Records are inserted with a date grange from now() to infinity.

To update a record, you call a stored procedure. It creates the new record with that same date range, and updates the old record to be valid up to (but not including) now(). The SP ensures the process is done correctly.

You can use the same date range in join tables for the same reason.

This makes it possible to see the full state of any record kept like this at any point in time, see when it was created, or last changed. An audit table records who changed it by holding the ID and timestamp of the change. There is no real deletion, you’d do soft deletion by setting a status.

I suspect this wouldn’t work well for very high volume tables without sharping or something. But for CRUD tables that don’t change a lot it’s fantastic.

The only thing that’s not smooth is future updates. If you need a new non-null column, it ends up added to all records. So you can either set a default and just deal with the fact that it’s now set on all old records, leave it as nullable and enforce non-null in code, or enforce it only on insert in a trigger or the SP I described.

I’ve found it much easier to use than some sort of ‘updates’ table storing JSON changes or EAV style updates or whatever.

  • This is a really good description of more or less exactly how our current approach works! This is a daily granularity variant we are testing atm, in order to eliminate flip-flops that occur during the length of a business day. The v1 impl was down to the second, this one is daily.

    Here is the core of it:

        CREATE TABLE time_travel_daily (
            domain              TEXT        NOT NULL,
            valid_range         tstzrange   NOT NULL,
            valid_from          timestamptz GENERATED ALWAYS AS (lower(valid_range)) STORED,
            valid_to            timestamptz GENERATED ALWAYS AS (upper(valid_range)) STORED,
    
            tld                     TEXT,
            owned                   BOOLEAN,
            acquired_at             timestamptz,
            released_at             timestamptz,
            registrar               TEXT,
            updated_at              timestamptz,
            accounting_uuid         TEXT,
            offer_received_date     timestamptz,
            payment_received_date   timestamptz,
            sold_at                 timestamptz,
            sold_channel            TEXT,
            last_renewed_at         timestamptz,
            expires_at              timestamptz,
            transfer_started_at     timestamptz,
            transfer_completed_at   timestamptz,
            transfer_eligible_at    timestamptz,
    
            snapshot_json           JSONB NOT NULL,
            inserted_at             timestamptz DEFAULT NOW() NOT NULL,
            source_data_change_id   INT,
    
            PRIMARY KEY (domain, valid_range)
        );
    
        CREATE INDEX ttd_domain_idx ON time_travel_daily(domain);
        CREATE INDEX ttd_gist_valid_range_idx ON time_travel_daily USING gist(valid_range);
        CREATE INDEX ttd_owned_valid_range_idx ON time_travel_daily USING gist(valid_range) WHERE owned = TRUE;
        CREATE INDEX ttd_registrar_idx ON time_travel_daily(registrar) WHERE registrar IS NOT NULL;
        CREATE INDEX ttd_source_data_change_id_idx ON time_travel_daily(source_data_change_id) WHERE source_data_change_id IS NOT NULL;
    
    

    And then here is a piece of our update trigger which "closes" previous entities and opens an new one:

        UPDATE time_travel_daily
        SET valid_range = tstzrange(lower(valid_range), target_date::timestamptz, '[)')
        WHERE domain IN (
            SELECT DISTINCT dc.domain
            FROM data_changes dc
            WHERE dc.invalidated IS NULL
                AND dc.after IS NOT NULL 
                AND dc.modified_at::date = target_date
        )
        AND upper(valid_range) IS NULL  -- Only close open ranges
        AND lower(valid_range) < target_date::timestamptz;  -- Don't close ranges that started today

    • A trigger to ‘close’ the old record is a great idea. My stored procedure is also doing some additional validation (validate at every layer = less bugs) so what I’ve got works well enough for me.

      But that’s very smart. Never considered it.

  • I'm not entirely sure what the valid_range is doing. Besides updating it, do you use this index for anything else? I agree the performance doesn't seem like it would be great.

    I do something like 4000 inserts a second, but maybe only a few queries a minute, so I use an "invalidated_by" column which (eventually) points to the newer record, and I update it on query instead of insert (when the multiple nulls are discovered and relevant)

    • The valid_range with a gist index is quite fast.

          SELECT COUNT(DISTINCT domain)
          FROM time_travel
          WHERE (CURRENT_DATE - INTERVAL '90 days')::timestamptz <@ valid_range
          AND owned;
      

      This is asking, "how many domains did we own 90 days ago"

      Instead of finding records where the start is less than, end is greater than, you can just say find me rows that will cover this point in time. The GiST index on valid_range does the heavy lifting.

      Lots of handy range-specific query tools available: https://www.postgresql.org/docs/17/functions-range.html

      1 reply →

> clean/erase/undo/soft-delete/hard-delete mistakes[...] make the change tracking capable of time travel itself [...] Transitioning to an EAV

I just finished building out all of that + more (e.g., data lineage, multi-verse, local overrides, etc), also on PG. Reach out if you want to chat and get nerd sniped!

For my little system (blog author here) I've decided that all tables will be append-only logs of facts. In the post, I tried keeping traditional schema alongside a "main" facts table. Of course, the problem of audit logs comes up for any change made to traditional "current-database-view" tables. And then who audit logs the audit log?

I feel like "two systems" is the problem.

Writes should either be traditional schema -> standard log of all changes, OR, should be "everything is a log", and the system keeps the "current" view updated, which is just a special case of the "as of" query, where "as of" is always "now".

How badly my schema will behave (in my SQLite-based architecture) is to be discovered. I will hopefully be in a position to run a reasonable benchmark next week.

A follow-up blog post is likely :sweat-smile:

  • > OR, should be "everything is a log",

    100%. This is a regret that I have in our lifecycle tracking. We effectively are updating a giant spreadsheet (table) and tracking side effects of those updates. I would much rather work in a log/event based system kinda flipping that on its head, where we track events like domain_renewed or domain_transferred.

    As it stands we track that the renewed_at attribute changed, and have to infer (along with other fields) things like "were you a renewal event?" which has changed meaning over time.

    Alas change tracking wasn't even part of the spec of this original feature... so I am glad I had the foresight to institute change tracking from 2022, as imperfect as it is currently.

    • So much this!

      Straight-up copy-paste from my development notes...

      * what pivots in current db vs temporal db?

      What is the difference between these pictures?

      #+BEGIN_SRC text

        ("Current" DB)
      
         CRUD records
               ^
               |
               v
        [ current view ] -- update --> [ facts log ]
      
        ---------------------------------------------------------
      
        [ current view ] <-- update -- [ facts log ]
               |                             ^
               v                             |
          READ records             WRITE records (as facts)
      
        ("Temporal" DB)
      

      #+END_SRC

      - Hint: It is /not/ the schema. It is /what/ "pivots".

      - In both cases the current view can be understood to be a pivot table of the facts log.

      - BUT in the current DB, we must "pivot" the /process/, i.e. take a CRUD op and transform it into an audit log. This /must/ be done synchronously in in real-time. Whereas in the Temporal DB's case, we must "pivot" the stored data, which we can do at any point in query time, as of any point in time of the log.

      - The complexity of Current DBs derives from /live process management/ challenges. Whereas the complexity of Temporal DBs derives from /retroactive process management/ challenges.

      /now/ is /never/ cheap. It is the most expensive non-fungible thing. Once it's gone, it's gone. Fail to transact an audit trail for the CRUD operation? Too bad. Better luck next time. Whereas disk space is cheap, and practically infinite which affords Temporal DBs greater opportunity to find a better trade-off between essential complexity and DB capabilities. At least as long as disk space remains plentiful and cheap.

      This is why if we are modeling a Temporal DB over a Current DB, it is preferable to write all tables as /fact/ tables and query their auto-generated 'current view' versions, for normal query needs. For audit / analysis needs, we can snapshot the facts tables and operate on those out-of-band (detached from the live app). Impedance mismatch occurs when trying to run /both/ current CRUD tables (writing to audit logs) for some parts of the schema, in parallel with a "main" facts table for all fact-records. In a given data system, it is better to do either one or the other, not both at the same time.

      2 replies →

  • Looking forward to the second blog!

    As a side project, I'm writing a CLI to tie environment variables to project directories ( https://github.com/bbkane/enventory ) and share env vars between projects with a "reference" system. Similar to direnv but in a central SQLite DB.

    See https://github.com/bbkane/enventory/tree/master/dbdoc for how the schema looks, but it's a few tables with foreign keys to each other to support the "reference" system.

    Unfortunately those foreign keys mean I don't have a way to "undo" changes easily. So instead I added a confirmation prompt, but I'd still like that undo button.

    Hopefully in subsequent blog posts you can follow up on your "Model an example domain of sufficient complexity" TODO and make this a bit less abstract (and hopefully more understandable) to me.

    • Maybe I could convert every table into a "log" table (i.e., append only similar to your blog) and add a timestamp column to each table (or UUID). Then I also store all of these UUIDs in another table to keep track of the global order. To "undo" I look up the previous UUID, find entries with that UUID, and append to the log table the values that make the state the same as the previous UUID....

      This isn't as general as your setup, but I think it would work ok with my foreign key issue.

      2 replies →