Files
dvc/dvc/database.py
T
skshetry 34d80fbfce add support for tracking remote dataset (#10287)
* add support for tracking remote dataset

This adds support for virtually tracking a
dataset such as dvcx dataset, dataset from remote dvc/git
registries, and cloud-versioned remotes.

This PR introduces two different commands under `ds` namespace:
`add` and `update`.

`dvc ds add` command adds the dataset to `dvc.yaml` and tracks
the sources in `dvc.lock` file. Similarly, `dvc ds update`
updates the sources in `dvc.lock` file.

Example Usage:
```console
dvc ds add --name dogs --url dvcx://dogs # tracking dvcx dataset
# tracking dvc/git registries
dvc ds add --name example --url dvc://[email protected]/iterative/example.git
# cloud-versioned-remotes
dvc ds add --name versioning --url s3://cloud-versioning-demo
```

To update, specify the name.
```console
dvc ds update <name>
```

`dvc ds add` freezes (in the traditional sense of `dvc import/import-url`).
It keeps a "specification" of the dataset in `dvc.yaml` and also freezes
information about the dataset in the `dvc.lock` file. They are kept
inside `datasets` section in both `dvc.yaml` and `dvc.lock` files.

This metadata is used in the pipelines. You can add a dependency to your
stage using `ds://` scheme, followed by the name of the dataset in
`dvc.yaml` file. As it is used in pipelines, the `name` of the dataset
has to be unique in the repository. Different dataset of same names
are not allowed. On `dvc repro`, `dvc` copies the frozen information
about the particular dataset into the `deps` field for the stage in
`dvc.lock`. On successive invocation, `dvc` will compare the information
from the deps field of the lock with the frozen information in the
`datasets` section and decides whether to rerun or not.

As the dataset is frozen, `dvc repro` won't rerun until the dataset is
updated via `dvc update`.

Here are some examples for how `dvc.yaml` looks like:
```yaml
datasets:
- name: dogs
  url: dvcx://dogs
  type: dvcx
- name: example-get-started
  url: [email protected]:iterative/example-get-started.git
  type: dvc
  path: path
- name: dogs2
  url: dvcx://dogs@v2
  type: dvcx
- name: cloud-versioning-demo
  url: s3://cloud-versioning-demo
  type: url
```

```yaml
schema: '2.0'
datasets:
- name: dogs
  url: dvcx://dogs
  type: dvcx
  version: 3
  created_at: '2023-12-11T10:32:05.942708+00:00'
- name: example-get-started
  url: [email protected]:iterative/example-get-started.git
  type: dvc
  path: path
  rev_lock: df75c16ef61f0772d6e4bb27ba4617b06b4b5398
- name: cloud-versioning-demo
  url: s3://cloud-versioning-demo
  type: url
  meta:
    isdir: true
    size: 323919
    nfiles: 33
  files:
  - relpath: myproject/model.pt
    meta:
      size: 106433
      version_id: 5qrtnhnQ4fBzV73kqqK6pMGhTOzd_IPr
      etag: 3bc0028677ce6fb65bec8090c248b002
# truncated
```

The pipeline stages keep them in `dataset` section of individual deps.

```console
dvc stage add -n train -d ds://dogs python train.py
cat dvc.yaml
```

```yaml
# truncated
stages:
  train:
    cmd: python train.py
    deps:
    - ds://dogs
```

When it is reproduced, the `dvc.lock` will look something like follows:
```yaml
# truncated
stages:
  train:
    cmd: python train.py
    deps:
    - path: ds://dogs
      dataset:
        name: dogs
        url: dvcx://dogs
        type: dvcx
        version: 3
        created_at: '2023-12-11T10:32:05.942708+00:00'
```

* remodeling

* remove non-cloud-versioned remotes

* improve deserializing; handle invalidation
2024-02-26 21:28:39 +05:45

135 lines
4.0 KiB
Python

import os
from collections.abc import Iterator
from contextlib import contextmanager
from dataclasses import dataclass
from tempfile import NamedTemporaryFile
from typing import TYPE_CHECKING, Any, Callable, Optional, Union
from sqlalchemy import create_engine # type: ignore[import]
from sqlalchemy.engine import make_url as _make_url # type: ignore[import]
from sqlalchemy.exc import NoSuchModuleError # type: ignore[import]
from dvc import env
from dvc.exceptions import DvcException
from dvc.log import logger
from dvc.types import StrOrBytesPath
from dvc.utils import env2bool
if TYPE_CHECKING:
from sqlalchemy.engine import URL, Connectable, Engine
from sqlalchemy.sql.expression import Selectable # type: ignore[import]
logger = logger.getChild(__name__)
def noop(_):
pass
def make_url(url: Union["URL", str], **kwargs: Any) -> "URL":
return _make_url(url).set(**kwargs)
def url_from_config(config: Union[str, "URL", dict[str, str]]) -> "URL":
if isinstance(config, dict):
return make_url(**config)
return make_url(config)
@contextmanager
def atomic_file(file: StrOrBytesPath, mode: str = "w+b"):
head, tail = os.path.split(os.fsdecode(file))
with NamedTemporaryFile(mode, prefix=tail + "-", dir=head, delete=False) as f:
yield f
os.replace(f.name, file)
@dataclass
class Serializer:
sql: "Union[str, Selectable]"
con: "Union[str, Connectable]"
chunksize: int = 10_000
def to_csv(self, file: StrOrBytesPath, progress=noop):
import pandas as pd
idfs = pd.read_sql(self.sql, self.con, chunksize=self.chunksize)
with atomic_file(file) as f:
for i, df in enumerate(idfs):
df.to_csv(f, header=i == 0, index=False)
progress(len(df))
def to_json(self, file: StrOrBytesPath, progress=noop): # noqa: ARG002
import pandas as pd
df = pd.read_sql(self.sql, self.con)
with atomic_file(file) as f:
df.to_json(f, orient="records")
def export(self, file: StrOrBytesPath, format: str = "csv", progress=noop): # noqa: A002
if format == "json":
return self.to_json(file, progress=progress)
return self.to_csv(file, progress=progress)
@dataclass
class Client:
engine: "Engine"
def test_connection(self, onerror: Optional[Callable[[], Any]] = None) -> None:
try:
with self.engine.connect() as conn:
conn.exec_driver_sql("select 1")
except Exception as exc:
if callable(onerror):
onerror()
logger.exception(
"Could not connect to the database. "
"Check your database credentials and try again.",
exc_info=False,
)
raise DvcException("The database returned the following error") from exc
def export(
self,
sql: "Union[str, Selectable]",
file: StrOrBytesPath,
format: str = "csv", # noqa: A002
progress=noop,
) -> None:
con = self.engine.connect()
if format == "csv":
con = con.execution_options(stream_results=True) # use server-side cursors
with con:
serializer = Serializer(sql, con)
return serializer.export(file, format=format, progress=progress)
@contextmanager
def handle_error(url: "URL"):
try:
yield
except (ModuleNotFoundError, NoSuchModuleError) as e:
# TODO: write installation instructions
driver = url.drivername
raise DvcException(f"Could not load database driver for {driver!r}") from e
@contextmanager
def client(
url_or_config: Union[str, "URL", dict[str, str]], **engine_kwargs: Any
) -> Iterator[Client]:
url = url_from_config(url_or_config)
echo = env2bool(env.DVC_SQLALCHEMY_ECHO, False)
engine_kwargs.setdefault("echo", echo)
with handle_error(url):
engine = create_engine(url, **engine_kwargs)
try:
yield Client(engine)
finally:
engine.dispose()