postgres_scanner VS ClickBench

I've created an ODBC DuckDB extension to query any database that has an ODBC driver. It's modeled after the fantastic official Postgres scanner extension https://github.com/duckdblabs/postgres_scanner.

It supports fetching rowsets in batches to minimize network overhead and defaults to the default DuckDB vector size of 2048.

I've tested it against the IBM DB2 & Postgres ODBC drivers and will continue to test and add support for all major databases. If you've got one you'd like to see let me know in the comments.

I've got plenty of improvements in the pipeline including:

It's not a dumb question at all. I'm pretty knowledgeable with DBs and still find it very difficult to understand how many of these front-end/pass-through engines work.

Checkout Postgres Foreign Data Wrappers. That might be the most well known approach for accessing one database through another. The Supabase team wrote an interesting piece about this recently.

https://supabase.com/blog/postgres-foreign-data-wrappers-rus...

You might also want to try out duckdb's approach to reading other DBs (or DB files). They talk about how they can "import" a sqlite DB in the above 0.7.0 announcement, but also have some other examples in their duckdblabs github project. Check out their "...-scanner" repos:

https://github.com/duckdblabs/postgres_scanner

https://github.com/duckdblabs/sqlite_scanner

Doesn't postgres have a columnar option? If so, you could prob get better performance for your analytical interactions if you switched some tables to columnar.

Otherwise check out postgres scanner. https://github.com/duckdblabs/postgres_scanner

DuckDB can actually read SQLite or Postgres directly! In the SQLite case, something like Litestream plus DuckDB could work really well!

Also, with Pyarrow's help, DuckDB can already do this with Delta tables!

https://github.com/duckdblabs/sqlite_scanner

https://github.com/duckdblabs/postgresscanner

Interesting thought! I have not tried this yet so I only have a guess as an answer. Could you export the data as SQL statements and then run those statements on DuckDB? That may be easier to set up, but may take longer to run...

DuckDB also has the ability to read Postgres data directly, and there is a Postgres FDW that can read from DuckDB!

https://github.com/duckdblabs/postgresscanner

https://github.com/alitrack/duckdb_fdw

TimescaleDB primarily serves operational use cases: Developers building products on top of live data, where you are regularly streaming in fresh data, and you often know what many queries look like a priori, because those are powering your live APIs, dashboards, and product experience.

That's different from a data warehouse or many traditional "OLAP" use cases, where you might dump a big dataset statically, and then people will occasionally do ad-hoc queries against it. This is the big weather dataset file sitting on your desktop that you occasionally query while on holidays.

So it's less about "can you store weather data", but what does that use case look like? How are the queries shaped? Are you saving a single dataset for ad-hoc queries across the entire dataset, or continuously streaming in new data, and aging out or de-prioritizing old data?

In most of the products we serve, customers are often interested in recent data in a very granular format ("shallow and wide"), or longer historical queries along a well defined axis ("deep and narrow").

For example, this is where the benefits of TimescaleDB's segmented columnar compression emerges. It optimizes for those queries which are very common in your application, e.g., an IoT application that groups by or selected by deviceID, crypto/fintech analysis based on the ticker symbol, product analytics based on tenantID, etc.

If you look at Clickbench, what most of the queries say are: Scan ALL the data in your database, and GROUP BY one of the 100 columns in the web analytics logs.

- https://github.com/ClickHouse/ClickBench/blob/main/clickhous...

There are almost no time-predicates in the benchmark that Clickhouse created, but perhaps that is not surprising given it was designed for ad-hoc weblog analytics at Yandex.

So yes, Timescale serves many products today that use weather data, but has made different choices than Clickhouse (or things like DuckDB, pg_analytics, etc) to serve those more operational use cases.

We tested with 43 Clickbench SQL queries. Queries on the Variant columns are about 10% slower than those on pre-defined static columns, and 8 times faster than those on JSON columns. (For I/O reasons, most cold runs on JSONB data failed with OOM.)

I have a project dedicated to this topic: https://github.com/ClickHouse/ClickBench

It is important to explain the limitations of a benchmark, provide a methodology, and make it reproducible. It also has to be simple enough, otherwise it will not be realistic to include a large number of participants.

I'm also collecting all database benchmarks I could find: https://github.com/ClickHouse/ClickHouse/issues/22398

Indeed, ClickHouse results were run on an older instance type of the same family and size (c5.4xlarge for ClickHouse and c6a.4xlarge for Timescale), so if anything ClickHouse results are at a slight disadvantage.

This is an open source benchmark - we'd love contributions from Timescale enthusiasts if we missed something: https://github.com/ClickHouse/ClickBench/

Interesting project! Thank you for open sourcing and sharing. Agree that local and embedded analytics are an increasing trend, I see it too.

A couple of questions:

* I’m curious what the difficulties were in the implementation. I suspect it is quite a challenge to implement this support in the current SQLite architecture, and would curious to know which parts were tricky and any design trade-off you were faced with.

* Aside from ease-of-use (install extension, no need for a separate analytical database system), I wonder if there are additional benefits users can anticipate resulting from a single system architecture vs running an embedded OLAP store like DuckDB or clickhouse-local / chdb side-by-side with SQLite? Do you anticipate performance or resource efficiency gains, for instance?

* I am also curious, what the main difficulty with bringing in a separate analytical database is, assuming it natively integrates with SQLite. I may be biased, but I doubt anything can approach the performance of native column-oriented systems, so I'm curious what the tipping point might be for using this extension vs using an embedded OLAP store in practice.

Btw, would love for you or someone in the community to benchmark Stanchion in ClickBench and submit results! (https://github.com/ClickHouse/ClickBench/)

Disclaimer: I work on ClickHouse.

interesting. curious if anyone has benchmarked it relative to other dbs. like: https://benchmark.clickhouse.com/