Minimal HTTP ClickHouse client for Elixir.
Used in Ecto ClickHouse adapter.
- RowBinary
- Native query parameters
- Per query settings
- Minimal API
Your ideas are welcome here.
defp deps do
[
{:ch, "~> 0.2.0"}
]
endStart DBConnection pool
defaults = [
scheme: "http",
hostname: "localhost",
port: 8123,
database: "default",
settings: [],
pool_size: 1,
timeout: :timer.seconds(15)
]
{:ok, pid} = Ch.start_link(defaults){:ok, pid} = Ch.start_link()
{:ok, %Ch.Result{rows: [[0], [1], [2]]}} =
Ch.query(pid, "SELECT * FROM system.numbers LIMIT 3")
{:ok, %Ch.Result{rows: [[0], [1], [2]]}} =
Ch.query(pid, "SELECT * FROM system.numbers LIMIT {$0:UInt8}", [3])
{:ok, %Ch.Result{rows: [[0], [1], [2]]}} =
Ch.query(pid, "SELECT * FROM system.numbers LIMIT {limit:UInt8}", %{"limit" => 3})Note on datetime encoding in query parameters:
%NaiveDateTime{}is encoded as text to make it assume the column's or ClickHouse server's timezone%DateTime{time_zone: "Etc/UTC"}is encoded as unix timestamp and is treated as UTC timestamp by ClickHouse- encoding non UTC
%DateTime{}raisesArgumentError
{:ok, pid} = Ch.start_link()
Ch.query!(pid, "CREATE TABLE IF NOT EXISTS ch_demo(id UInt64) ENGINE Null")
%Ch.Result{num_rows: 2} =
Ch.query!(pid, "INSERT INTO ch_demo(id) VALUES (0), (1)")
%Ch.Result{num_rows: 2} =
Ch.query!(pid, "INSERT INTO ch_demo(id) VALUES ({$0:UInt8}), ({$1:UInt32})", [0, 1])
%Ch.Result{num_rows: 2} =
Ch.query!(pid, "INSERT INTO ch_demo(id) VALUES ({a:UInt16}), ({b:UInt64})", %{"a" => 0, "b" => 1})
%Ch.Result{num_rows: 2} =
Ch.query!(pid, "INSERT INTO ch_demo(id) SELECT number FROM system.numbers LIMIT {limit:UInt8}", %{"limit" => 2})Insert rows as RowBinary (efficient)
{:ok, pid} = Ch.start_link()
Ch.query!(pid, "CREATE TABLE IF NOT EXISTS ch_demo(id UInt64) ENGINE Null")
types = ["UInt64"]
# or
types = [Ch.Types.u64()]
# or
types = [:u64]
%Ch.Result{num_rows: 2} =
Ch.query!(pid, "INSERT INTO ch_demo(id) FORMAT RowBinary", [[0], [1]], types: types)Note that RowBinary format encoding requires :types option to be provided.
Similarly, you can use RowBinaryWithNamesAndTypes which would additionally do something like a type check.
sql = "INSERT INTO ch_demo FORMAT RowBinaryWithNamesAndTypes"
opts = [names: ["id"], types: ["UInt64"]]
rows = [[0], [1]]
%Ch.Result{num_rows: 2} = Ch.query!(pid, sql, rows, opts)Insert rows in custom format
{:ok, pid} = Ch.start_link()
Ch.query!(pid, "CREATE TABLE IF NOT EXISTS ch_demo(id UInt64) ENGINE Null")
csv = [0, 1] |> Enum.map(&to_string/1) |> Enum.intersperse(?\n)
%Ch.Result{num_rows: 2} =
Ch.query!(pid, "INSERT INTO ch_demo(id) FORMAT CSV", csv, encode: false){:ok, pid} = Ch.start_link()
Ch.query!(pid, "CREATE TABLE IF NOT EXISTS ch_demo(id UInt64) ENGINE Null")
stream = Stream.repeatedly(fn -> [:rand.uniform(100)] end)
chunked = Stream.chunk_every(stream, 100)
encoded = Stream.map(chunked, fn chunk -> Ch.RowBinary.encode_rows(chunk, _types = ["UInt64"]) end)
ten_encoded_chunks = Stream.take(encoded, 10)
%Ch.Result{num_rows: 1000} =
Ch.query(pid, "INSERT INTO ch_demo(id) FORMAT RowBinary", ten_encoded_chunks, encode: false)This query makes a transfer-encoding: chunked HTTP request while unfolding the stream resulting in lower memory usage.
Query with custom settings
{:ok, pid} = Ch.start_link()
settings = [async_insert: 1]
%Ch.Result{rows: [["async_insert", "Bool", "0"]]} =
Ch.query!(pid, "SHOW SETTINGS LIKE 'async_insert'")
%Ch.Result{rows: [["async_insert", "Bool", "1"]]} =
Ch.query!(pid, "SHOW SETTINGS LIKE 'async_insert'", [], settings: settings)It's the same as in ch-go
At insert time, Nil can be passed for both the normal and Nullable version of a column. For the former, the default value for the type will be persisted, e.g., an empty string for string. For the nullable version, a NULL value will be stored in ClickHouse.
{:ok, pid} = Ch.start_link()
Ch.query!(pid, """
CREATE TABLE ch_nulls (
a UInt8 NULL,
b UInt8 DEFAULT 10,
c UInt8 NOT NULL
) ENGINE Memory
""")
types = ["Nullable(UInt8)", "UInt8", "UInt8"]
inserted_rows = [[nil, nil, nil]]
selected_rows = [[nil, 0, 0]]
%Ch.Result{num_rows: 1} =
Ch.query!(pid, "INSERT INTO ch_nulls(a, b, c) FORMAT RowBinary", inserted_rows, types: types)
%Ch.Result{rows: ^selected_rows} =
Ch.query!(pid, "SELECT * FROM ch_nulls")Note that in this example DEFAULT 10 is ignored and 0 (the default value for UInt8) is persisted instead.
However, input() can be used as a workaround:
sql = """
INSERT INTO ch_nulls
SELECT * FROM input('a Nullable(UInt8), b Nullable(UInt8), c UInt8')
FORMAT RowBinary\
"""
Ch.query!(pid, sql, inserted_rows, types: ["Nullable(UInt8)", "Nullable(UInt8)", "UInt8"])
%Ch.Result{rows: [[0], [10]]} =
Ch.query!(pid, "SELECT b FROM ch_nulls ORDER BY b")When decoding String columns non UTF-8 characters are replaced with � (U+FFFD). This behaviour is similar to toValidUTF8 and JSON format.
{:ok, pid} = Ch.start_link()
Ch.query!(pid, "CREATE TABLE ch_utf8(str String) ENGINE Memory")
bin = "\x61\xF0\x80\x80\x80b"
utf8 = "a�b"
%Ch.Result{num_rows: 1} =
Ch.query!(pid, "INSERT INTO ch_utf8(str) FORMAT RowBinary", [[bin]], types: ["String"])
%Ch.Result{rows: [[^utf8]]} =
Ch.query!(pid, "SELECT * FROM ch_utf8")
%Ch.Result{rows: %{"data" => [[^utf8]]}} =
pid |> Ch.query!("SELECT * FROM ch_utf8 FORMAT JSONCompact") |> Map.update!(:rows, &Jason.decode!/1)To get raw binary from String columns use :binary type that skips UTF-8 checks.
%Ch.Result{rows: [[^bin]]} =
Ch.query!(pid, "SELECT * FROM ch_utf8", [], types: [:binary])Decoding non-UTC datetimes like DateTime('Asia/Taipei') requires a timezone database.
Mix.install([:ch, :tz])
:ok = Calendar.put_time_zone_database(Tz.TimeZoneDatabase)
{:ok, pid} = Ch.start_link()
%Ch.Result{rows: [[~N[2023-04-25 17:45:09]]]} =
Ch.query!(pid, "SELECT CAST(now() as DateTime)")
%Ch.Result{rows: [[~U[2023-04-25 17:45:11Z]]]} =
Ch.query!(pid, "SELECT CAST(now() as DateTime('UTC'))")
%Ch.Result{rows: [[%DateTime{time_zone: "Asia/Taipei"} = taipei]]} =
Ch.query!(pid, "SELECT CAST(now() as DateTime('Asia/Taipei'))")
"2023-04-26 01:45:12+08:00 CST Asia/Taipei" = to_string(taipei)Encoding non-UTC datetimes raises an ArgumentError
Ch.query!(pid, "CREATE TABLE ch_datetimes(datetime DateTime) ENGINE Null")
naive = NaiveDateTime.utc_now()
utc = DateTime.utc_now()
taipei = DateTime.shift_zone!(utc, "Asia/Taipei")
# ** (ArgumentError) non-UTC timezones are not supported for encoding: 2023-04-26 01:49:43.044569+08:00 CST Asia/Taipei
Ch.query!(pid, "INSERT INTO ch_datetimes(datetime) FORMAT RowBinary", [[naive], [utc], [taipei]], types: ["DateTime"])INSERT 1 million rows (original)
$ MIX_ENV=bench mix run bench/insert.exs
This benchmark is based on https://github.com/ClickHouse/clickhouse-go#benchmark
Operating System: macOS
CPU Information: Apple M1
Number of Available Cores: 8
Available memory: 8 GB
Elixir 1.14.4
Erlang 25.3
Benchmark suite executing with the following configuration:
warmup: 2 s
time: 5 s
memory time: 0 ns
reduction time: 0 ns
parallel: 1
inputs: 1_000_000 rows
Estimated total run time: 28 s
Benchmarking encode with input 1_000_000 rows ...
Benchmarking encode stream with input 1_000_000 rows ...
Benchmarking insert with input 1_000_000 rows ...
Benchmarking insert stream with input 1_000_000 rows ...
##### With input 1_000_000 rows #####
Name ips average deviation median 99th %
encode stream 1.63 612.96 ms ±11.30% 583.03 ms 773.01 ms
insert stream 1.22 819.82 ms ±9.41% 798.94 ms 973.45 ms
encode 1.09 915.75 ms ±44.13% 750.98 ms 1637.02 ms
insert 0.73 1373.84 ms ±31.01% 1331.86 ms 1915.76 ms
Comparison:
encode stream 1.63
insert stream 1.22 - 1.34x slower +206.87 ms
encode 1.09 - 1.49x slower +302.79 ms
insert 0.73 - 2.24x slower +760.88 ms
SELECT 500, 500 thousand, and 500 million rows (original)
$ MIX_ENV=bench mix run bench/stream.exs
This benchmark is based on https://github.com/ClickHouse/ch-bench
Operating System: macOS
CPU Information: Apple M1
Number of Available Cores: 8
Available memory: 8 GB
Elixir 1.14.4
Erlang 25.3
Benchmark suite executing with the following configuration:
warmup: 2 s
time: 5 s
memory time: 0 ns
reduction time: 0 ns
parallel: 1
inputs: 500 rows, 500_000 rows, 500_000_000 rows
Estimated total run time: 1.05 min
Benchmarking stream with decode with input 500 rows ...
Benchmarking stream with decode with input 500_000 rows ...
Benchmarking stream with decode with input 500_000_000 rows ...
Benchmarking stream with manual decode with input 500 rows ...
Benchmarking stream with manual decode with input 500_000 rows ...
Benchmarking stream with manual decode with input 500_000_000 rows ...
Benchmarking stream without decode with input 500 rows ...
Benchmarking stream without decode with input 500_000 rows ...
Benchmarking stream without decode with input 500_000_000 rows ...
##### With input 500 rows #####
Name ips average deviation median 99th %
stream with decode 4.69 K 213.34 μs ±12.49% 211.38 μs 290.94 μs
stream with manual decode 4.69 K 213.43 μs ±17.40% 210.96 μs 298.75 μs
stream without decode 4.65 K 215.08 μs ±10.79% 213.79 μs 284.66 μs
Comparison:
stream with decode 4.69 K
stream with manual decode 4.69 K - 1.00x slower +0.0838 μs
stream without decode 4.65 K - 1.01x slower +1.74 μs
##### With input 500_000 rows #####
Name ips average deviation median 99th %
stream without decode 234.58 4.26 ms ±13.99% 4.04 ms 5.95 ms
stream with manual decode 64.26 15.56 ms ±8.36% 15.86 ms 17.97 ms
stream with decode 41.03 24.37 ms ±6.27% 24.39 ms 26.60 ms
Comparison:
stream without decode 234.58
stream with manual decode 64.26 - 3.65x slower +11.30 ms
stream with decode 41.03 - 5.72x slower +20.11 ms
##### With input 500_000_000 rows #####
Name ips average deviation median 99th %
stream without decode 0.32 3.17 s ±0.20% 3.17 s 3.17 s
stream with manual decode 0.0891 11.23 s ±0.00% 11.23 s 11.23 s
stream with decode 0.0462 21.66 s ±0.00% 21.66 s 21.66 s
Comparison:
stream without decode 0.32
stream with manual decode 0.0891 - 3.55x slower +8.06 s
stream with decode 0.0462 - 6.84x slower +18.50 s
CI Results (click the latest workflow run and scroll down to "Artifacts")