| Name | kolat JSON |
| Version |
2.0.0
JSON |
| download |
| home_page | None |
| Summary | a Python Polars interface to j* and q |
| upload_time | 2025-08-16 05:24:46 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
j*
q
kdb
polars
dataframe
arrow
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# kola
a Python [Polars](https://pola-rs.github.io/polars/) Interface to `j*` and `q`
## Basic Data Type Map
### j\*
#### Deserialization
##### Atom
| j type | n | size | python type | note |
| ----------- | --- | ---- | ----------- | --------------------------- |
| `boolean` | 1 | 1 | `bool` | |
| `u8` | 4 | 1 | `int` | |
| `i16` | 5 | 2 | `int` | |
| `i32` | 6 | 4 | `int` | |
| `i64` | 7 | 8 | `int` | |
| `f32` | 8 | 4 | `float` | |
| `f64` | 9 | 8 | `float` | |
| `string` | 10 | 1 | `str` | |
| `symbol` | 11 | \* | `str` | |
| `timestamp` | 12 | 8 | `datetime` | |
| `date` | 14 | 4 | `date` | 0001.01.01 - 9999.12.31 |
| `datetime` | 15 | 8 | `datetime` | |
| `duration` | 16 | 8 | `timedelta` | |
| `time` | 19 | 4 | `time` | 00:00:00.000 - 23:59:59.999 |
##### Composite Data Type
| k type | n | size | python type |
| ------------ | ---- | ---- | -------------- |
| `series` | 1-15 | - | `pl.Series` |
| `list` | 90 | - | `Tuple` |
| `dictionary` | 91 | \* | `dict` |
| `dataframe` | 92 | \* | `pl.DataFrame` |
#### Serialization
##### Basic Data Type
| python type | j type | note |
| ----------- | ---------- | --------------------------- |
| `bool` | `boolean` | |
| `int` | `i64` | |
| `float` | `f64` | |
| `str` | `symbol` | |
| `bytes` | `string` | |
| `date` | `date` | 0001.01.01 - 9999.12.31 |
| `datetime` | `datetime` | |
| `timedelta` | `duration` | |
| `time` | `time` | 00:00:00.000 - 23:59:59.999 |
##### Dictionary, Series and DataFrame
| python type | j type |
| -------------- | --------- |
| `dict` | dict |
| `pl.Series` | series |
| `pl.DataFrame` | dataframe |
> for dictionary, requires `string` as keys.
### q
#### Deserialization
##### Atom
| k type | n | size | python type | note |
| ----------- | --- | ---- | ----------- | --------------------------- |
| `boolean` | 1 | 1 | `bool` | |
| `guid` | 2 | 16 | `str` | |
| `byte` | 4 | 1 | `int` | |
| `short` | 5 | 2 | `int` | |
| `int` | 6 | 4 | `int` | |
| `long` | 7 | 8 | `int` | |
| `real` | 8 | 4 | `float` | |
| `float` | 9 | 8 | `float` | |
| `char` | 10 | 1 | `str` | |
| `string` | 10 | 1 | `str` | |
| `symbol` | 11 | \* | `str` | |
| `timestamp` | 12 | 8 | `datetime` | |
| `month` | 13 | 4 | `-` | |
| `date` | 14 | 4 | `date` | 0001.01.01 - 9999.12.31 |
| `datetime` | 15 | 8 | `datetime` | |
| `timespan` | 16 | 8 | `timedelta` | |
| `minute` | 17 | 4 | `time` | 00:00 - 23:59 |
| `second` | 18 | 4 | `time` | 00:00:00 - 23:59:59 |
| `time` | 19 | 4 | `time` | 00:00:00.000 - 23:59:59.999 |
##### Composite Data Type
| k type | n | size | python type |
| ---------------- | --- | ---- | ------------------------ |
| `boolean list` | 1 | 1 | `pl.Boolean` |
| `guid list` | 2 | 16 | `pl.List(pl.Binary(16))` |
| `byte list` | 4 | 1 | `pl.Uint8` |
| `short list` | 5 | 2 | `pl.Int16` |
| `int list` | 6 | 4 | `pl.Int32` |
| `long list` | 7 | 8 | `pl.Int64` |
| `real list` | 8 | 4 | `pl.Float32` |
| `float list` | 9 | 8 | `pl.Float64` |
| `char list` | 10 | 1 | `pl.Utf8` |
| `string list` | 10 | 1 | `pl.Utf8` |
| `symbol list` | 11 | \* | `pl.Categorical` |
| `timestamp list` | 12 | 8 | `pl.Datetime` |
| `month list` | 13 | 4 | `-` |
| `date list` | 14 | 4 | `pl.Date` |
| `datetime list` | 15 | 8 | `pl.Datetime` |
| `timespan list` | 16 | 8 | `pl.Duration` |
| `minute list` | 17 | 4 | `pl.Time` |
| `second list` | 18 | 4 | `pl.Time` |
| `time list` | 19 | 4 | `pl.Time` |
| `table` | 98 | \* | `pl.DataFrame` |
| `dictionary` | 99 | \* | `-` |
| `keyed table` | 99 | \* | `pl.DataFrame` |
> performance is impacted by converting guid to string, deserialize the uuid to 16 fixed binary list, use .hex() to convert binary to string if required
> real/float 0n is mapped to Polars null not NaN
> short/int/long 0Nh/i/j, 0Wh/i/j and -0Wh/i/j are mapped to null
```
df.with_columns([
(pl.col("uuid").apply(lambda u: u.hex()))
])
```
#### Serialization
##### Basic Data Type
| python type | k type | note |
| ----------- | ----------- | --------------------------- |
| `bool` | `boolean` | |
| `int` | `long` | |
| `float` | `float` | |
| `str` | `symbol` | |
| `bytes` | `string` | |
| `datetime` | `timestamp` | |
| `date` | `date` | 0001.01.01 - 9999.12.31 |
| `datetime` | `datetime` | |
| `timedelta` | `timespan` | |
| `time` | `time` | 00:00:00.000 - 23:59:59.999 |
##### Dictionary, Series and DataFrame
| python type | k type |
| ------------------------ | --------- |
| `dict` | dict |
| `pl.Boolean` | boolean |
| `pl.List(pl.Binary(16))` | guid |
| `pl.Uint8` | byte |
| `pl.Int16` | short |
| `pl.Int32` | int |
| `pl.Int64` | long |
| `pl.Float32` | real |
| `pl.Float64` | float |
| `pl.Utf8` | char |
| `pl.Categorical` | symbol |
| `pl.Datetime` | timestamp |
| `pl.Date` | date |
| `pl.Datetime` | datetime |
| `pl.Duration` | timespan |
| `pl.Time` | time |
| `pl.DataFrame` | table |
> Limited Support for dictionary as arguments, requires `string` as keys.
## Quick Start
### Create a Connection
```python
import polars as pl
import kola
# Connect to j*, J and Q both work with j*
conn = kola.J('localhost', 1800)
# Connect to q
conn = kola.Q('localhost', 1800)
# with retries for IO Errors, 1s, 2s, 4s ...
conn = kola.J('localhost', 1800, retries=3)
# with read timeout error, 2s, "Resource temporarily unavailable"
conn = kola.J('localhost', 1800, retries=3, timeout=2)
```
### Connect(Optional)
Automatically connect when querying q process
```python
conn.connect()
```
### Disconnect
Automatically disconnect if any IO error
```python
conn.disconnect()
```
### String Query
```python
conn.sync("select from trade where date=last date")
```
### Functional Query
For functional query, `kola` supports Python [Basic Data Type](#basic-data-type), `pl.Series`, `pl.DataFrame` and Python Dictionary with string keys and Python [Basic Data Type](#basic-data-type) and `pl.Series` values.
```python
from datetime import date, time
conn.sync(
".gw.query",
"table",
{
"date": date(2023, 11, 21),
"syms": pl.Series("", ["sym0", "sym1"], pl.Categorical),
# 09:00
"startTime": time(9),
# 11:30
"endTime": time(11, 30),
},
)
```
### Send DataFrame
```python
# pl_df is a Polars DataFrame
conn.sync("upsert", "table", pl_df)
```
```python
# pd_df is a Pandas DataFrame, use pl.DateFrame to cast Pandas DataFrame
conn.sync("upsert", "table", pl.DataFrame(pd_df))
```
### Async Query
```python
# pl_df is a Polars DataFrame
conn.asyn("upsert", "table", pl_df)
```
### Subscribe
```python
from kola import QType
conn.sync(".u.sub", pl.Series("", ["table1", "table2"], QType.Symbol), "")
# specify symbol filter
conn.sync(
".u.sub",
pl.Series("", ["table1", "table2"], QType.Symbol),
pl.Series("", ["sym1", "sym2"], QType.Symbol),
)
while true:
# ("upd", "table", pl.Dataframe)
upd = conn.receive()
print(upd)
```
### Generate IPC for q
```python
import polars as pl
from kola import serialize_as_ipc_bytes6
df = pl.DataFrame(
{
"sym": pl.Series("sym", ["a", "b", "c"], pl.Categorical),
"price": [1, 2, 3],
}
)
# without compression
buffer = serialize_as_ipc_bytes6("sync", False, ["upd", "table", df])
# with compression
buffer = serialize_as_ipc_bytes6("sync", True, ["upd", "table", df])
```
## Polars Documentations
Refer to
- [User Guide](https://pola-rs.github.io/polars/user-guide/)
- [API Reference](https://pola-rs.github.io/polars/py-polars/html/reference/index.html)
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"description": "# kola\n\na Python [Polars](https://pola-rs.github.io/polars/) Interface to `j*` and `q`\n\n## Basic Data Type Map\n\n### j\\*\n\n#### Deserialization\n\n##### Atom\n\n| j type | n | size | python type | note |\n| ----------- | --- | ---- | ----------- | --------------------------- |\n| `boolean` | 1 | 1 | `bool` | |\n| `u8` | 4 | 1 | `int` | |\n| `i16` | 5 | 2 | `int` | |\n| `i32` | 6 | 4 | `int` | |\n| `i64` | 7 | 8 | `int` | |\n| `f32` | 8 | 4 | `float` | |\n| `f64` | 9 | 8 | `float` | |\n| `string` | 10 | 1 | `str` | |\n| `symbol` | 11 | \\* | `str` | |\n| `timestamp` | 12 | 8 | `datetime` | |\n| `date` | 14 | 4 | `date` | 0001.01.01 - 9999.12.31 |\n| `datetime` | 15 | 8 | `datetime` | |\n| `duration` | 16 | 8 | `timedelta` | |\n| `time` | 19 | 4 | `time` | 00:00:00.000 - 23:59:59.999 |\n\n##### Composite Data Type\n\n| k type | n | size | python type |\n| ------------ | ---- | ---- | -------------- |\n| `series` | 1-15 | - | `pl.Series` |\n| `list` | 90 | - | `Tuple` |\n| `dictionary` | 91 | \\* | `dict` |\n| `dataframe` | 92 | \\* | `pl.DataFrame` |\n\n#### Serialization\n\n##### Basic Data Type\n\n| python type | j type | note |\n| ----------- | ---------- | --------------------------- |\n| `bool` | `boolean` | |\n| `int` | `i64` | |\n| `float` | `f64` | |\n| `str` | `symbol` | |\n| `bytes` | `string` | |\n| `date` | `date` | 0001.01.01 - 9999.12.31 |\n| `datetime` | `datetime` | |\n| `timedelta` | `duration` | |\n| `time` | `time` | 00:00:00.000 - 23:59:59.999 |\n\n##### Dictionary, Series and DataFrame\n\n| python type | j type |\n| -------------- | --------- |\n| `dict` | dict |\n| `pl.Series` | series |\n| `pl.DataFrame` | dataframe |\n\n> for dictionary, requires `string` as keys.\n\n### q\n\n#### Deserialization\n\n##### Atom\n\n| k type | n | size | python type | note |\n| ----------- | --- | ---- | ----------- | --------------------------- |\n| `boolean` | 1 | 1 | `bool` | |\n| `guid` | 2 | 16 | `str` | |\n| `byte` | 4 | 1 | `int` | |\n| `short` | 5 | 2 | `int` | |\n| `int` | 6 | 4 | `int` | |\n| `long` | 7 | 8 | `int` | |\n| `real` | 8 | 4 | `float` | |\n| `float` | 9 | 8 | `float` | |\n| `char` | 10 | 1 | `str` | |\n| `string` | 10 | 1 | `str` | |\n| `symbol` | 11 | \\* | `str` | |\n| `timestamp` | 12 | 8 | `datetime` | |\n| `month` | 13 | 4 | `-` | |\n| `date` | 14 | 4 | `date` | 0001.01.01 - 9999.12.31 |\n| `datetime` | 15 | 8 | `datetime` | |\n| `timespan` | 16 | 8 | `timedelta` | |\n| `minute` | 17 | 4 | `time` | 00:00 - 23:59 |\n| `second` | 18 | 4 | `time` | 00:00:00 - 23:59:59 |\n| `time` | 19 | 4 | `time` | 00:00:00.000 - 23:59:59.999 |\n\n##### Composite Data Type\n\n| k type | n | size | python type |\n| ---------------- | --- | ---- | ------------------------ |\n| `boolean list` | 1 | 1 | `pl.Boolean` |\n| `guid list` | 2 | 16 | `pl.List(pl.Binary(16))` |\n| `byte list` | 4 | 1 | `pl.Uint8` |\n| `short list` | 5 | 2 | `pl.Int16` |\n| `int list` | 6 | 4 | `pl.Int32` |\n| `long list` | 7 | 8 | `pl.Int64` |\n| `real list` | 8 | 4 | `pl.Float32` |\n| `float list` | 9 | 8 | `pl.Float64` |\n| `char list` | 10 | 1 | `pl.Utf8` |\n| `string list` | 10 | 1 | `pl.Utf8` |\n| `symbol list` | 11 | \\* | `pl.Categorical` |\n| `timestamp list` | 12 | 8 | `pl.Datetime` |\n| `month list` | 13 | 4 | `-` |\n| `date list` | 14 | 4 | `pl.Date` |\n| `datetime list` | 15 | 8 | `pl.Datetime` |\n| `timespan list` | 16 | 8 | `pl.Duration` |\n| `minute list` | 17 | 4 | `pl.Time` |\n| `second list` | 18 | 4 | `pl.Time` |\n| `time list` | 19 | 4 | `pl.Time` |\n| `table` | 98 | \\* | `pl.DataFrame` |\n| `dictionary` | 99 | \\* | `-` |\n| `keyed table` | 99 | \\* | `pl.DataFrame` |\n\n> performance is impacted by converting guid to string, deserialize the uuid to 16 fixed binary list, use .hex() to convert binary to string if required\n\n> real/float 0n is mapped to Polars null not NaN\n\n> short/int/long 0Nh/i/j, 0Wh/i/j and -0Wh/i/j are mapped to null\n\n```\ndf.with_columns([\n (pl.col(\"uuid\").apply(lambda u: u.hex()))\n ])\n```\n\n#### Serialization\n\n##### Basic Data Type\n\n| python type | k type | note |\n| ----------- | ----------- | --------------------------- |\n| `bool` | `boolean` | |\n| `int` | `long` | |\n| `float` | `float` | |\n| `str` | `symbol` | |\n| `bytes` | `string` | |\n| `datetime` | `timestamp` | |\n| `date` | `date` | 0001.01.01 - 9999.12.31 |\n| `datetime` | `datetime` | |\n| `timedelta` | `timespan` | |\n| `time` | `time` | 00:00:00.000 - 23:59:59.999 |\n\n##### Dictionary, Series and DataFrame\n\n| python type | k type |\n| ------------------------ | --------- |\n| `dict` | dict |\n| `pl.Boolean` | boolean |\n| `pl.List(pl.Binary(16))` | guid |\n| `pl.Uint8` | byte |\n| `pl.Int16` | short |\n| `pl.Int32` | int |\n| `pl.Int64` | long |\n| `pl.Float32` | real |\n| `pl.Float64` | float |\n| `pl.Utf8` | char |\n| `pl.Categorical` | symbol |\n| `pl.Datetime` | timestamp |\n| `pl.Date` | date |\n| `pl.Datetime` | datetime |\n| `pl.Duration` | timespan |\n| `pl.Time` | time |\n| `pl.DataFrame` | table |\n\n> Limited Support for dictionary as arguments, requires `string` as keys.\n\n## Quick Start\n\n### Create a Connection\n\n```python\nimport polars as pl\nimport kola\n# Connect to j*, J and Q both work with j*\nconn = kola.J('localhost', 1800)\n\n# Connect to q\nconn = kola.Q('localhost', 1800)\n\n# with retries for IO Errors, 1s, 2s, 4s ...\nconn = kola.J('localhost', 1800, retries=3)\n\n# with read timeout error, 2s, \"Resource temporarily unavailable\"\nconn = kola.J('localhost', 1800, retries=3, timeout=2)\n```\n\n### Connect(Optional)\n\nAutomatically connect when querying q process\n\n```python\nconn.connect()\n```\n\n### Disconnect\n\nAutomatically disconnect if any IO error\n\n```python\nconn.disconnect()\n```\n\n### String Query\n\n```python\nconn.sync(\"select from trade where date=last date\")\n```\n\n### Functional Query\n\nFor functional query, `kola` supports Python [Basic Data Type](#basic-data-type), `pl.Series`, `pl.DataFrame` and Python Dictionary with string keys and Python [Basic Data Type](#basic-data-type) and `pl.Series` values.\n\n```python\nfrom datetime import date, time\n\nconn.sync(\n \".gw.query\",\n \"table\",\n {\n \"date\": date(2023, 11, 21),\n \"syms\": pl.Series(\"\", [\"sym0\", \"sym1\"], pl.Categorical),\n # 09:00\n \"startTime\": time(9),\n # 11:30\n \"endTime\": time(11, 30),\n },\n)\n```\n\n### Send DataFrame\n\n```python\n# pl_df is a Polars DataFrame\nconn.sync(\"upsert\", \"table\", pl_df)\n```\n\n```python\n# pd_df is a Pandas DataFrame, use pl.DateFrame to cast Pandas DataFrame\nconn.sync(\"upsert\", \"table\", pl.DataFrame(pd_df))\n```\n\n### Async Query\n\n```python\n# pl_df is a Polars DataFrame\nconn.asyn(\"upsert\", \"table\", pl_df)\n```\n\n### Subscribe\n\n```python\nfrom kola import QType\n\nconn.sync(\".u.sub\", pl.Series(\"\", [\"table1\", \"table2\"], QType.Symbol), \"\")\n\n# specify symbol filter\nconn.sync(\n \".u.sub\",\n pl.Series(\"\", [\"table1\", \"table2\"], QType.Symbol),\n pl.Series(\"\", [\"sym1\", \"sym2\"], QType.Symbol),\n)\n\nwhile true:\n # (\"upd\", \"table\", pl.Dataframe)\n upd = conn.receive()\n print(upd)\n```\n\n### Generate IPC for q\n\n```python\nimport polars as pl\nfrom kola import serialize_as_ipc_bytes6\n\ndf = pl.DataFrame(\n {\n \"sym\": pl.Series(\"sym\", [\"a\", \"b\", \"c\"], pl.Categorical),\n \"price\": [1, 2, 3],\n }\n)\n# without compression\nbuffer = serialize_as_ipc_bytes6(\"sync\", False, [\"upd\", \"table\", df])\n\n# with compression\nbuffer = serialize_as_ipc_bytes6(\"sync\", True, [\"upd\", \"table\", df])\n```\n\n## Polars Documentations\n\nRefer to\n\n- [User Guide](https://pola-rs.github.io/polars/user-guide/)\n- [API Reference](https://pola-rs.github.io/polars/py-polars/html/reference/index.html)\n\n",
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