The DuckDB Julia package provides a high-performance front-end for DuckDB. Much like SQLite, DuckDB runs in-process within the Julia client, and provides a DBInterface front-end.
The package also supports multi-threaded execution. It uses Julia threads/tasks for this purpose. If you wish to run queries in parallel, you must launch Julia with multi-threading support (by e.g., setting the JULIA_NUM_THREADS environment variable).
Installation
Install DuckDB as follows:
using Pkg
Pkg.add("DuckDB")
Alternatively, enter the package manager using the ] key, and issue the following command:
pkg> add DuckDB
Basics
using DuckDB
# create a new in-memory database
con = DBInterface.connect(DuckDB.DB, ":memory:")
# create a table
DBInterface.execute(con, "CREATE TABLE integers (i INTEGER)")
# insert data by executing a prepared statement
stmt = DBInterface.prepare(con, "INSERT INTO integers VALUES(?)")
DBInterface.execute(stmt, [42])
# query the database
results = DBInterface.execute(con, "SELECT 42 a")
print(results)
Some SQL statements, such as PIVOT and IMPORT DATABASE are executed as multiple prepared statements and will error when using DuckDB.execute(). Instead they can be run with DuckDB.query() instead of DuckDB.execute() and will always return a materialized result.
Scanning DataFrames
The DuckDB Julia package also provides support for querying Julia DataFrames. Note that the DataFrames are directly read by DuckDB - they are not inserted or copied into the database itself.
If you wish to load data from a DataFrame into a DuckDB table you can run a CREATE TABLE ... AS or INSERT INTO query.
using DuckDB
using DataFrames
# create a new in-memory dabase
con = DBInterface.connect(DuckDB.DB)
# create a DataFrame
df = DataFrame(a = [1, 2, 3], b = [42, 84, 42])
# register it as a view in the database
DuckDB.register_data_frame(con, df, "my_df")
# run a SQL query over the DataFrame
results = DBInterface.execute(con, "SELECT * FROM my_df")
print(results)
Appender API
The DuckDB Julia package also supports the Appender API, which is much faster than using prepared statements or individual INSERT INTO statements. Appends are made in row-wise format. For every column, an append() call should be made, after which the row should be finished by calling flush(). After all rows have been appended, close() should be used to finalize the Appender and clean up the resulting memory.
using DuckDB, DataFrames, Dates
db = DuckDB.DB()
# create a table
DBInterface.execute(db,
"CREATE OR REPLACE TABLE data(id INTEGER PRIMARY KEY, value FLOAT, timestamp TIMESTAMP, date DATE)")
# create data to insert
len = 100
df = DataFrames.DataFrame(
id = collect(1:len),
value = rand(len),
timestamp = Dates.now() + Dates.Second.(1:len),
date = Dates.today() + Dates.Day.(1:len)
)
# append data by row
appender = DuckDB.Appender(db, "data")
for i in eachrow(df)
for j in i
DuckDB.append(appender, j)
end
DuckDB.end_row(appender)
end
# close the appender after all rows
DuckDB.close(appender)
Concurrency
Within a Julia process, tasks are able to concurrently read and write to the database, as long as each task maintains its own connection to the database. In the example below, a single task is spawned to periodically read the database and many tasks are spawned to write to the database using both INSERT statements as well as the Appender API.
using Dates, DataFrames, DuckDB
db = DuckDB.DB()
DBInterface.connect(db)
DBInterface.execute(db, "CREATE OR REPLACE TABLE data (date TIMESTAMP, id INTEGER)")
function run_reader(db)
# create a DuckDB connection specifically for this task
conn = DBInterface.connect(db)
while true
println(DBInterface.execute(conn,
"SELECT id, count(date) AS count, max(date) AS max_date
FROM data GROUP BY id ORDER BY id") |> DataFrames.DataFrame)
Threads.sleep(1)
end
DBInterface.close(conn)
end
# spawn one reader task
Threads.@spawn run_reader(db)
function run_inserter(db, id)
# create a DuckDB connection specifically for this task
conn = DBInterface.connect(db)
for i in 1:1000
Threads.sleep(0.01)
DuckDB.execute(conn, "INSERT INTO data VALUES (current_timestamp, ?)"; id);
end
DBInterface.close(conn)
end
# spawn many insert tasks
for i in 1:100
Threads.@spawn run_inserter(db, 1)
end
function run_appender(db, id)
# create a DuckDB connection specifically for this task
appender = DuckDB.Appender(db, "data")
for i in 1:1000
Threads.sleep(0.01)
row = (Dates.now(Dates.UTC), id)
for j in row
DuckDB.append(appender, j);
end
DuckDB.end_row(appender);
end
DuckDB.close(appender);
end
# spawn many appender tasks
for i in 1:100
Threads.@spawn run_appender(db, 2)
end
Original Julia Connector
Credits to kimmolinna for the original DuckDB Julia connector.