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Historical.batch.submit_job(
    dataset: Dataset | str,
    symbols: Iterable[str | int] | str | int,
    schema: Schema | str,
    start: pd.Timestamp | datetime | date | str | int,
    end: pd.Timestamp | datetime | date | str | int | None = None,
    encoding: Encoding | str = "dbn",
    compression: Compression | str = "zstd",
    pretty_px: bool = False,
    pretty_ts: bool = False,
    map_symbols: bool | None = None,
    split_symbols: bool = False,
    split_duration: Duration | str = "day",
    split_size: int | None = None,
    delivery: Delivery | str = "download",
    stype_in: SType | str = "raw_symbol",
    stype_out: SType | str = "instrument_id",
    limit: int | None = None,
) -> dict[str, Any]

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import databento as db

client = db.Historical("$YOUR_API_KEY")

details = client.batch.submit_job(
    dataset="GLBX.MDP3",
    symbols=["ESM2"],
    schema="trades",
    encoding="dbn",
    start="2022-06-06T12:00:00+00:00",
    end="2022-06-10T00:00:00+00:00",
)
print(details)

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{
    "id": "GLBX-20221217-MN5S5S4WAS",
    "user_id": "NBPDLF33",
    "api_key": "prod-001",
    "cost_usd": None,
    "dataset": "GLBX.MDP3",
    "symbols": "ESM2",
    "stype_in": "raw_symbol",
    "stype_out": "instrument_id",
    "schema": "trades",
    "start": "2022-06-06T12:00:00.000000000Z",
    "end": "2022-06-10T00:00:00.000000000Z",
    "limit": None,
    "encoding": "dbn",
    "compression": "zstd",
    "pretty_px": False,
    "pretty_ts": False,
    "map_symbols": False,
    "split_symbols": False,
    "split_duration": "day",
    "split_size": None,
    "packaging": None,
    "delivery": "download",
    "record_count": None,
    "billed_size": None,
    "actual_size": None,
    "package_size": None,
    "state": "queued",
    "ts_received": "2022-12-17T00:36:37.844913000Z",
    "ts_queued": None,
    "ts_process_start": None,
    "ts_process_done": None,
    "ts_expiration": None
}

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Historical.batch.list_jobs(
    states: Iterable[JobState | str] | JobState | str | None = "queued,processing,done",
    since: pd.Timestamp | datetime | date | str | int | None = None,
    short: bool | None = None,
) -> list[dict[str, Any]]

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import databento as db

client = db.Historical("$YOUR_API_KEY")

jobs = client.batch.list_jobs(
    states=["queued", "processing", "done"],
    since="2022-06-01",
)
print(jobs)

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[
    {
        "id": "GLBX-20221126-DBVXWPJJQN",
        "state": "done",
        "ts_received": "2022-11-26T09:23:17.519708000Z"
    },
...

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Historical.batch.list_files(
    job_id: str,
) -> list[dict[str, Any]]

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import databento as db

client = db.Historical("$YOUR_API_KEY")

files = client.batch.list_files(job_id="GLBX-20220610-5DEFXVTMSM")
print(files)

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[
    {
        "filename": "metadata.json",
        "size": 1102,
        "hash": "sha256:0168d53e1705b69b1d6407f10bb3ab48aac492fa0f68f863cc9b092931cc67a7",
        "urls": {
            "https": "https://api.databento.com/v0/batch/download/46PCMCVF/GLBX-20230203-WF9WJYSCDU/metadata.json",
            "ftp": "ftp://ftp.databento.com/46PCMCVF/GLBX-20230203-WF9WJYSCDU/metadata.json",
        }
    },
    {
        "filename": "glbx-mdp3-20220610.mbo.csv.zst",
        "size": 21832,
        "hash": "sha256:1218930af153b4953632216044ef87607afa467fc7ab7fbb1f031fceacf9d52a",
        "urls": {
            "https": "https://api.databento.com/v0/batch/download/46PCMCVF/GLBX-20230203-WF9WJYSCDU/glbx-mdp3-20220610.mbo.csv.zst",
            "ftp": "ftp://ftp.databento.com/46PCMCVF/GLBX-20230203-WF9WJYSCDU/glbx-mdp3-20220610.mbo.csv.zst",
        }
    }
]

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Historical.batch.get_job_details(
    job_id: str,
) -> dict[str, Any]

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import databento as db

client = db.Historical("$YOUR_API_KEY")

job_details = client.batch.get_job_details(
    job_id="GLBX-20221126-DBVXWPJJQN",
)

print(job_details)

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{
    "id": "GLBX-20221126-DBVXWPJJQN",
    "user_id": "NBPDLF33",
    "api_key": "prod-001",
    "cost_usd": 23.6454,
    "dataset": "GLBX.MDP3",
    "symbols": "ZC.FUT,ES.FUT",
    "stype_in": "parent",
    "stype_out": "instrument_id",
    "schema": "mbo",
    "start": "2022-10-24T00:00:00.000000000Z",
    "end": "2022-11-24T00:00:00.000000000Z",
    "limit": None,
    "encoding": "csv",
    "compression": "zstd",
    "pretty_px": False,
    "pretty_ts": False,
    "map_symbols": False,
    "split_symbols": False,
    "split_duration": "day",
    "split_size": None,
    "packaging": None,
    "delivery": "download",
    "record_count": 412160224,
    "billed_size": 23080972544,
    "actual_size": 8144595219,
    "package_size": 8144628684,
    "state": "done",
    "ts_received": "2022-11-26T09:23:17.519708000Z",
    "ts_queued": "2022-12-03T14:34:57.897790000Z",
    "ts_process_start": "2022-12-03T14:35:00.495167000Z",
    "ts_process_done": "2022-12-03T14:48:15.710116000Z",
    "ts_expiration": "2023-01-02T14:48:15.710116000Z",
    "progress": 100
}

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Historical.batch.download(
    job_id: str,
    output_dir: PathLike[str] | str | None = None,
    filename_to_download: str | None = None,
    keep_zip: bool = False,
) -> list[Path]

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import databento as db

client = db.Historical("$YOUR_API_KEY")

# Download all files for the batch job
client.batch.download(
    job_id="GLBX-20220610-5DEFXVTMSM",
    output_dir="my_data/",
)

# Alternatively, you can download a specific file
client.batch.download(
    job_id="GLBX-20220610-5DEFXVTMSM",
    output_dir="my_data/",
    filename_to_download="metadata.json",
)

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Historical.batch.download_async(
    job_id: str,
    output_dir: PathLike[str] | str | None = None,
    filename_to_download: str | None = None,
    keep_zip: bool = False,
) -> Awaitable[list[Path]]

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import asyncio
import databento as db

client = db.Historical("$YOUR_API_KEY")

# Download all files for the batch job
coro = client.batch.download_async(
    job_id="GLBX-20220610-5DEFXVTMSM",
    output_dir="my_data/",
)
asyncio.run(coro)

# Alternatively, you can download a specific file
coro = client.batch.download_async(
    job_id="GLBX-20220610-5DEFXVTMSM",
    output_dir="my_data/",
    filename_to_download="metadata.json",
)
asyncio.run(coro)