Connection

geneva.db.Connection

Bases: DBConnection

Geneva Connection.

namespace_client_impl

namespace_client_impl: str | None

namespace_client_properties

namespace_client_properties: dict[str, str] | None

namespace_client_pushdown_operations

namespace_client_pushdown_operations: list[str] | None

system_namespace

system_namespace: list[str]

user_db_uri

user_db_uri: str

Original user database URI for this connection.

flight_client

flight_client: Any

close

close() -> None

Close the connection.

alter_or_create_system_table

alter_or_create_system_table(
    table_name: str,
    model: Any,
    namespace: list[str] | None = None,
) -> tuple[Connection, list[str], Table]

Open or create a Geneva internal table in its resolved location.

namespace_client

namespace_client() -> LanceNamespace

Returns namespace client from the underlying LanceDB connection.

table_names

table_names(
    page_token: str | None = None,
    limit: int | None = None,
    *args,
    **kwargs,
) -> Iterable[str]

List all available tables and views.

list_tables

list_tables(
    namespace_path: list[str] | None = None,
    page_token: str | None = None,
    limit: int | None = None,
) -> ListTablesResponse

List all tables in this database with pagination support.

Parameters:

• namespace_path (list[str], default: None ) –

  The namespace to list tables in. None or empty list represents root namespace.

• page_token (str, default: None ) –

  Token for pagination. Use the token from a previous response to get the next page of results.

• limit (int, default: None ) –

  The maximum number of results to return.

Returns:

• ListTablesResponse –

  Response containing table names and optional page_token for pagination.

open_table

open_table(
    name: str,
    storage_options: dict[str, str] | None = None,
    index_cache_size: int | None = None,
    version: int | None = None,
    namespace: list[str] | None = None,
    *args,
    **kwargs,
) -> Table

Open a Lance Table.

Parameters:

• name (str) –

  Name of the table.

• storage_options (dict[str, str] | None, default: None ) –

  Additional options for the storage backend. Options already set on the connection will be inherited by the table, but can be overridden here. See available options at https://lancedb.github.io/lancedb/guides/storage/

• namespace (list[str] | None, default: None ) –

  Namespace path for the table (e.g., ["workspace"])

create_table

create_table(
    name: str,
    data: DATA | None = None,
    schema: Schema | LanceModel | None = None,
    mode: str = "create",
    exist_ok: bool = False,
    on_bad_vectors: str = "error",
    fill_value: float = 0.0,
    storage_options: dict[str, str] | None = None,
    *args,
    **kwargs,
) -> Table

Create a Table in the lake

Parameters:

• name (str) –

  The name of the table

• data (DATA | None, default: None ) –

  User must provide at least one of data or schema. Acceptable types are:

  • list-of-dict
  • pandas.DataFrame
  • pyarrow.Table or pyarrow.RecordBatch
• schema (Schema | LanceModel | None, default: None ) –

  Acceptable types are:

  • pyarrow.Schema
  • lancedb.pydantic.LanceModel
• mode (str, default: 'create' ) –

  The mode to use when creating the table. Can be either "create" or "overwrite". By default, if the table already exists, an exception is raised. If you want to overwrite the table, use mode="overwrite".

• exist_ok (bool, default: False ) –

  If a table by the same name already exists, then raise an exception if exist_ok=False. If exist_ok=True, then open the existing table; it will not add the provided data but will validate against any schema that's specified.

• on_bad_vectors (str, default: 'error' ) –

  What to do if any of the vectors are not the same size or contain NaNs. One of "error", "drop", "fill".

create_view

create_view(
    name: str, query: str, materialized: bool = False
) -> Table

Create a View from a Query.

Parameters:

• name (str) –

  Name of the view.

• query (str) –

  SQL query to create the view.

• materialized (bool, default: False ) –

  If True, the view is materialized.

create_materialized_view

create_materialized_view(
    name: str,
    query: GenevaQueryBuilder,
    with_no_data: bool = True,
    *,
    auto_refresh: bool = False,
) -> Table

Create a materialized view

Parameters:

• name (str) –

  Name of the materialized view.

• query (GenevaQueryBuilder) –

  Query to create the view.

• with_no_data (bool, default: True ) –

  If True, the view is materialized, if false it is ready for refresh.

• auto_refresh (bool, default: False ) –

  If True, the server will refresh the view when source-table data changes past the deployment-level threshold. Honored only for remote (db://) connections.

create_udtf_view

create_udtf_view(
    name: str,
    source: GenevaQueryBuilder,
    udtf: UDTF | Chunker,
    *,
    with_no_data: bool = True,
    auto_refresh: bool = False,
) -> Table

Create a UDTF-backed materialized view.

.. warning:: This API is in beta and may change in future releases.

Pass an @udtf-decorated object for a batch UDTF MV (N:M rows, full-overwrite refresh). Pass an @chunker-decorated object for a chunker MV (1:N row expansion, incremental refresh). The two kinds differ in output-schema shape and refresh semantics; this method picks the right one from type(udtf).

The view is created empty (UDTF) or with placeholder rows (chunker); call view.refresh() to populate it.

Parameters:

• name (str) –

  Name for the new view table.

• source (GenevaQueryBuilder) –

  Query defining the source data.

• udtf (UDTF | Chunker) –

  The UDTF or chunker to execute on refresh.

• with_no_data (bool, default: True ) –

  If True (default), the view is created empty / with placeholders. If False, the server starts an initial refresh immediately. Honored only for remote (db://) connections.

• auto_refresh (bool, default: False ) –

  If True, the server will refresh the view when source-table data changes past the deployment-level threshold. Honored only for remote (db://) connections.

drop_view

drop_view(name: str) -> Table

Drop a view.

drop_table

drop_table(name: str, *args, **kwargs) -> None

Drop a table.

define_cluster

define_cluster(name: str, cluster: GenevaCluster) -> None

Define a persistent Geneva cluster. This will upsert the cluster definition by name. The cluster can then be provisioned using context(cluster=name).

Parameters:

• name (str) –

  Name of the cluster. This will be used as the key when upserting and provisioning the cluster. The cluster name must comply with RFC 12123.

• cluster (GenevaCluster) –

  The cluster definition to store.

list_clusters

list_clusters() -> list[GenevaCluster]

List the cluster definitions. These can be defined using define_cluster().

Returns:

• list[GenevaCluster] –

  List of Geneva cluster definitions

delete_cluster

delete_cluster(name: str) -> None

Delete a Geneva cluster definition.

Parameters:

• name (str) –

  Name of the cluster to delete.

define_manifest

define_manifest(
    name: str,
    manifest: GenevaManifest,
    uploader: Uploader | None = None,
) -> None

Define a persistent Geneva Manifest that represents the files and dependencies used in the execution environment. This will upsert the manifest definition by name and upload the required artifacts. The manifest can then be used with context(manifest=name).

Parameters:

• name (str) –

  Name of the manifest. This will be used as the key when upserting and loading the manifest.

• manifest (GenevaManifest) –

  The manifest definition to use.

• uploader (Uploader | None, default: None ) –

  An optional, custom Uploader to use. If not provided, the uploader will be auto-detected based on the environment configuration.

list_manifests

list_manifests() -> list[GenevaManifest]

List the manifest definitions. These can be defined using define_manifest().

Returns:

• list[GenevaManifest] –

  List of Geneva manifest definitions

delete_manifest

delete_manifest(name: str) -> None

Delete a Geneva manifest definition.

Parameters:

• name (str) –

  Name of the manifest to delete.

capture_local_environment

capture_local_environment(
    name: str | None = None,
    *,
    skip_site_packages: bool = False,
) -> GenevaManifest

Capture and upload the caller's local environment.

Zips the workspace (and, by default, site-packages) and uploads the resulting archives through this connection's namespace-vended Uploader before returning. The returned :class:~geneva.manifest.GenevaManifest carries the uploaded zip URIs and is ready to be passed to @udf(manifest=...).

Note: the query node deployment must be configured for credential-vending via vend_input_storage_options configuration

Typical usage::

db = geneva.connect(...)
manifest = db.capture_local_environment(skip_site_packages=True)

@udf(manifest=manifest)
def embed(text: str) -> list[float]: ...

Use :meth:GenevaManifest.create_pip for declarative manifests when you do not want any local-environment upload to happen.

Parameters:

• name (str | None, default: None ) –

  Optional manifest name. If omitted, an auto-generated name is assigned.

• skip_site_packages (bool, default: False ) –

  If True, capture only the workspace source and rely on the worker image's pre-installed dependencies.

Returns:

• GenevaManifest –

  A manifest with zips populated. No further resolution step is required.

Raises:

• RuntimeError –

  If this connection cannot vend an Uploader (e.g. a db:// connection without namespace_client_impl configured).

local_ray_context

local_ray_context() -> AbstractContextManager[None]

Context manager for a local Ray instance. This will provision a local Ray instance and return a context manager. This is useful for development or small jobs.

context

context(
    cluster: str,
    manifest: str | None = None,
    on_exit=None,
    wait_timeout: float | None = None,
    log_to_driver: bool = True,
    logging_level=INFO,
) -> AbstractContextManager[None]

Context manager for a Geneva Execution Environment. This will provision a cluster based on the cluster definition and the manifest provided. By default, the context manager will delete the cluster on exit. This can be configured with the on_exit parameter.

Parameters:

• cluster (str) –

  Name of the persisted cluster definition to use. Required. This will raise an exception if the cluster definition was not defined via define_cluster().

• manifest (str | None, default: None ) –

  Optional name of the persisted manifest to use. This will raise an exception if the manifest definition was not defined via define_manifest(). If manifest is not provided, the local environment will be uploaded.

• on_exit –

  Exit mode for the cluster. By default, the cluster waits for all running jobs to complete before deleting. To retain the cluster when any job fails or the context body raises an exception, use ExitMode.RETAIN_ON_FAILURE. To always retain the cluster, use ExitMode.RETAIN.

• wait_timeout (float | None, default: None ) –

  Internal/experimental. Maximum seconds to wait for tracked jobs during context exit. Only applies with DELETE or RETAIN_ON_FAILURE. None means wait indefinitely. For RETAIN_ON_FAILURE, a timeout is treated as a failure and the cluster is retained.

• log_to_driver (bool, default: True ) –

  Whether to send Ray worker logs to the driver. Defaults to True for better visibility in tests and debugging.

• logging_level –

  The logging level for Ray workers. Use logging.DEBUG for detailed logs.

is_remote_uri

is_remote_uri() -> bool

is_remote

is_remote() -> bool

use_remote_dispatch

use_remote_dispatch() -> bool

get_job

get_job(job_id: str) -> JobRecord

Get a job record by ID.

Reads from the _geneva_jobs system table via JobStateManager. Works for both native and remote connections.

list_jobs

list_jobs(
    table_name: str | None = None, status: str | None = None
) -> list[JobRecord]

List jobs, optionally filtered by table and/or status.

Reads from the _geneva_jobs system table via JobStateManager. Works for both native and remote connections.