DuckDB is a high performance in-process database system. It is a natural pairing for tech.ml.dataset which is a high performance column-major in-memory dataframe system similar to pandas or R's data table. DuckDB provides perhaps cleaner pathways to load/save parquet files as you don't need to navigate the minefield of dependencies required to use parquet from Clojure.
user> (require '[tech.v3.dataset :as ds])
nil
user> (def stocks
(ds/->dataset "https://github.com/techascent/tech.ml.dataset/raw/master/test/data/stocks.csv"
{:key-fn keyword
:dataset-name :stocks}))
#'user/stocks
user> (ds/head stocks)
:stocks [5 3]:
| :symbol | :date | :price |
|---------|------------|-------:|
| MSFT | 2000-01-01 | 39.81 |
| MSFT | 2000-02-01 | 36.35 |
| MSFT | 2000-03-01 | 43.22 |
| MSFT | 2000-04-01 | 28.37 |
| MSFT | 2000-05-01 | 25.45 |
user> (require '[tmducken.duckdb :as duckdb])
nil
user> (duckdb/initialize!)
Aug 31, 2023 8:50:21 AM clojure.tools.logging$eval5800$fn__5803 invoke
INFO: Attempting to load duckdb from "./binaries/libduckdb.so"
true
user> (def db (duckdb/open-db))
#'user/db
user> (def conn (duckdb/connect db))
#'user/conn
user> (duckdb/create-table! conn stocks)
"stocks"
user> (duckdb/insert-dataset! conn stocks)
560
user> (ds/head (duckdb/sql->dataset conn \"select * from stocks\"))
_unnamed [5 3]:
| symbol | date | price |
|--------|------------|------:|
| MSFT | 2000-01-01 | 39.81 |
| MSFT | 2000-02-01 | 36.35 |
| MSFT | 2000-03-01 | 43.22 |
| MSFT | 2000-04-01 | 28.37 |
| MSFT | 2000-05-01 | 25.45 |
user> (def stmt (duckdb/prepare conn \"select * from stocks\"))
Aug 31, 2023 8:52:25 AM clojure.tools.logging$eval5800$fn__5803 invoke
INFO: Reference thread starting
#'user/stmt
user> stmt
#duckdb-prepared-statement-0[\"select * from stocks\"]
user> (stmt)
#duckdb-streaming-result[\"select * from stocks\"]
user> (ds/head (first *1))
_unnamed [5 3]:
| symbol | date | price |
|--------|------------|------:|
| MSFT | 2000-01-01 | 39.81 |
| MSFT | 2000-02-01 | 36.35 |
| MSFT | 2000-03-01 | 43.22 |
| MSFT | 2000-04-01 | 28.37 |
| MSFT | 2000-05-01 | 25.45 |This system requires the user to install/manage the duckdb binary dependency. DuckDB also supplies a JDBC driver (documentation) so this library is entirely optional as you can use the jdbc driver with tmd's existing jdbc integration.
What this library provides beyond the JDBC pathway is very fast transfer from the duckdb query result, which is stored already in column-major form, to a dataset. The JDBC driver forces you to go row by row and essentially use the sequence-of-maps->dataset pathway. This library is also another example of how to use dtype-next's ffi system to bind to a C library.
One thing to note is that because duckdb already supplies the query result columns to the user in data form there is no advantage to using apache arrow as a query result.
You can get a jar for your own OS (Linux/MacOS) with duckdb included by installing Nix, then running:
$ nix build github:techascent/tmducken
$ uname -s -p
Darwin arm
$ jar tf result/tmducken.jar | grep libduckdb.dylib
darwin-aarch64/libduckdb.dylibYou can include this in your deps.edn via: {:local/root "result/tmducken.jar"}
With this method, you can invoke (duckdb/initialize!) normally and it will load the shared library automatically.
First, download binaries and set either install then into your system path or set the DUCKDB_HOME environment variable to where the shared library is installed - see enable-duckdb script. Linux users can simply type:
source scripts/enable-duckdbNext, you should be able to call initialize! in the duckdb namespace. Be sure to read the namespace documentation and perhaps peruse the unit tests.
Whenever the deps.edn file changes, you have to run deps-lock, as provided by the nix shell.
For more details see: https://jlesquembre.github.io/clj-nix/lock-file/
Not doing so fails local Nix builds (nix build) by trying to fetch dependencies in a non-network build environment.
Alternative CI-based solution to the above: https://jlesquembre.github.io/clj-nix/github-action/