JSON Changelog with SQLite(blog.budgetwithbuckets.com) |
JSON Changelog with SQLite(blog.budgetwithbuckets.com) |
Each time, I wonder why people are recreating the database's journal, within the database, where this meta-journal will itself be getting journaled.
Why not just consume the actual DB journal?
I'm not sure how that'd work for SQLite—it'd probably require a hack of some sort, since SQLite's journal is an ephemeral part of the DB file. But for more full-fledged RDBMSes, this would just be a matter of hijacking a streaming-replication-based backup system to act as a store of record for events. E.g., in the case of Postgres, you'd just have an ETL pipeline pointed at your WAL-E bucket, parsing through your WAL files either to load into a separate OLAP store, or for stateless map-reduction whenever you need to answer a question.
It's decidedly non-trivial to do so, as usually the journal doesn't contain all the information to do so. Journal writes are often a bottleneck so DB engines try to restrict their contents to just the necessary parts. Typically it'll e.g. not contain information about table schemas etc.
You can do so, see e.g. postgresql's logical decoding, but it's plenty of additional work.
The point I was trying to make is that the alternative—writing all your DB activity into the DB—means that you're already, 100% guaranteed, creating more overhead than the heaviest possible journal-safety properties would create. You're writing unabridged journal entries into the DB, where they then get even more journal attached.
There is no world where it makes more sense to write a copy of everything you do into an "everything I did" table, than it does to use an append-only / immutable / log-centric DBMS (where "Postgres with an ETL pipeline pointed at its WAL logs" is a rather hacky immutable DBMS.)
If you know, before you ever start coding, that you will need to know everything that ever happened, writing everything twice—when your DBMS is already writing everything twice—is a rather silly way to choose to go about solving your problems. You're already in a world where you need different guarantees than a traditional (ephemeral) RDBMS gives you; you just haven't realized it yet.
select * from journal
select * from index_customer
My dream is build one more flexible, starting with a relational lang:
https://bitbucket.org/tablam/tablam/wiki/Home
But this is still in prototyping.
I have lots of wishes in that direction. For example, I wish that my programs could ask 'has this table been written to since I last checked' instead of having to read all the price lists into the application cache every n minutes whether there have been any changes or not etc.
I only want READ access to the change logs and audit logs...if databases only did this, it would be so much better.
Though, I get what you mean... the format in question would allow for querying changes against a given table for the last 5. With your solution, you'd have to go through the whole journal to track all the changes for a given record, and present it in a history format, with say diffing.
You can query the oplog yourself easily [0]. I also remember someone doing a version for MySql using the same approach.
[0] http://www.briancarpio.com/2012/04/21/mongodb-the-oplog-expl...
import sqlite3
def delta(*args):
ret = {}
for name, old, new in zip(args[::3], args[1::3], args[2::3]):
if old != new:
ret[name] = old
if ret:
return ret
db = sqlite3.connect(':memory:')
db.create_function('delta1', delta, 3)
db.create_function('delta2', delta, 6)
db.create_function('delta3', delta, 9)
print db.execute('select delta2("b", "20", "20", "c", 3.4, 3.6)').fetchone()
>> (u'{"c": 3.4}',) import sqlite3
import json
def delta(*args):
ret = {}
for name, old, new in zip(args[::3], args[1::3],
args[2::3]):
if old != new:
ret[name] = old
if ret:
return json.dumps(ret)
db = sqlite3.connect(':memory:')
db.create_function('delta1',3, delta)
db.create_function('delta2',6, delta)
db.create_function('delta3',9, delta)
print(db.execute('select delta2("b", 20, 20, "c", 3.4,
3.6)').fetchone())
# ('{"c": 3.4}',)For instance, Microsoft SQL Server has a built-in public change tracking API, which you can tell is a leaky abstraction over top of the journal, but SQL Server makes an API guarantee when using that specific change tracking API whereas (at least the last time I looked) it will never make API guarantees about anything lower level than that that accesses its journal. The Change Tracking API documentation even warns you that it locks the journal into certain deoptimizations and that there are clear performance trade-offs, which also hints that using the lower level journal API more directly would likely hit optimization problems and data loss issues.
As a dev, it's rarely a good idea to program against APIs your vendor intentionally deems to be private/implementation details that can change between versions/may be unstable in runtime operation. If the database doesn't offer a public journaling API, then yes, sadly, sometimes the best or at least least-worst answer is to build a sub-par journal inside the database itself, with of course its own tradeoffs in performance.
Postgres' logical decoding [1] provides something very similar.
[1]: https://www.postgresql.org/docs/current/static/logicaldecodi...
There is one: the world where append-only / immutable / log-centric DBMS do not offer the same advantages as mainstream RDBMSs - scalability, stability, performance, language support, open sourcing, active development, ubiquitous SQL knowledge, etc. etc.
The downside of bolting an event-sourcing or changelog system onto an established RDBMS like Postgres are (a) the cost of writing them, and (b) the performance / disk space cost. Both of those may be easier to deal with than, say, Datomic's licensing and Clojure-oriented APIs, especially if you can use an existing library like Marten.
What immutable DBMS would you recommend?
The GP specifically said the DBMS journal does not write everything: it writes as little as it can get away with, for performance reasons. Since you quoted GP very selectively, you didn’t address what they actually said.