Struct rerun_bindings::remote::PyStorageNodeClient

pub struct PyStorageNodeClient {
    runtime: Runtime,
    client: StorageNodeClient<Channel>,
}

A connection to a remote storage node.

Fields

runtime: Runtime

A tokio runtime for async operations. This connection will currently block the Python interpreter while waiting for responses. This runtime must be persisted for the lifetime of the connection.

client: StorageNodeClient<Channel>

The actual tonic connection.

Implementations

impl PyStorageNodeClient

fn get_store_info(&mut self, id: &str) -> PyResult<StoreInfo>

Get the StoreInfo for a single recording in the storage node.

pub(crate) fn exec_query( &mut self, id: StoreId, query: QueryExpression, ) -> PyResult<PyArrowType<Box<dyn RecordBatchReader + Send>>>

Execute a QueryExpression for a single recording in the storage node.

impl PyStorageNodeClient

fn query_catalog( &mut self, columns: Option<Vec<String>>, recording_ids: Option<Vec<String>>, ) -> PyResult<PyArrowType<Box<dyn RecordBatchReader + Send>>>

Get the metadata for recordings in the storage node.

Parameters

columns : Optional[liststr] The columns to fetch. If None, fetch all columns. recording_ids : Optional[liststr] Fetch metadata of only specific recordings. If None, fetch for all.

fn get_recording_schema(&mut self, id: String) -> PyResult<PySchema>

Get the schema for a recording in the storage node.

Parameters

id : str The id of the recording to get the schema for.

Returns

Schema The schema of the recording.

fn register( &mut self, storage_url: &str, metadata: Option<MetadataLike>, ) -> PyResult<String>

Register a recording along with some metadata.

Parameters

storage_url : str The URL to the storage location. metadata : Optional[Table | RecordBatch] A pyarrow Table or RecordBatch containing the metadata to update. This Table must contain only a single row.

fn create_vector_index( &mut self, entry: String, column: PyComponentColumnSelector, time_index: PyIndexColumnSelector, num_partitions: u32, num_sub_vectors: u32, distance_metric: VectorDistanceMetricLike, ) -> PyResult<()>

Create a vector index.

Parameters

entry : str The name of the catalog entry to index. column : ComponentColumnSelector The component column to index. time_index : IndexColumnSelector The index column to use for the time index. num_partitions : int The number of partitions for the index. num_sub_vectors : int The number of sub-vectors for the index. distance_metric : VectorDistanceMetric The distance metric to use for the index.

fn create_fts_index( &mut self, entry: String, column: PyComponentColumnSelector, time_index: PyIndexColumnSelector, store_position: bool, base_tokenizer: &str, ) -> PyResult<()>

Create a full-text-search index.

Parameters

entry : str The name of the catalog entry to index. column : ComponentColumnSelector The component column to index. time_index : IndexColumnSelector The index column to use for the time index. store_position : bool Whether to store the position of the token in the document. base_tokenizer : str The base tokenizer to use.

fn search_vector_index( &mut self, entry: String, query: VectorLike<'_>, column: PyComponentColumnSelector, top_k: u32, ) -> PyResult<PyArrowType<Box<dyn RecordBatchReader + Send>>>

Search over a vector index.

Parameters

entry : str The name of the catalog entry to search. query : VectorLike The input to search for. column : ComponentColumnSelector The component column to search over. top_k : int The number of results to return.

Returns

pa.RecordBatchReader The results of the query.

fn search_fts_index( &mut self, entry: String, query: String, column: PyComponentColumnSelector, limit: Option<u32>, ) -> PyResult<PyArrowType<Box<dyn RecordBatchReader + Send>>>

Search over a full-text-search index.

Parameters

entry : str The name of the catalog entry to search. query : str The input to search for. column : ComponentColumnSelector The component column to search over. limit : Optional[int] The maximum number of results to return.

Returns

pa.RecordBatchReader The results of the query.

fn update_catalog(&mut self, metadata: MetadataLike) -> PyResult<()>

Update the catalog metadata for one or more recordings.

The updates are provided as a pyarrow Table or RecordBatch containing the metadata to update. The Table must contain an ‘id’ column, which is used to specify the recording to update for each row.

Parameters

metadata : Table | RecordBatch A pyarrow Table or RecordBatch containing the metadata to update.

fn open_recording(slf: Bound<'_, Self>, id: &str) -> PyResult<PyRemoteRecording>

Open a [Recording][rerun.dataframe.Recording] by id to use with the dataframe APIs.

This will run queries against the remote storage node and stream the results. Faster for small numbers of queries with small results.

Parameters

id : str The id of the recording to open.

Returns

Recording The opened recording.

fn download_recording(&mut self, id: &str) -> PyResult<PyRecording>

Download a [Recording][rerun.dataframe.Recording] by id to use with the dataframe APIs.

This will download the full recording to memory and run queries against a local chunk store.

Parameters

id : str The id of the recording to open.

Returns

Recording The opened recording.

Trait Implementations

impl IntoPy<Py<PyAny>> for PyStorageNodeClient

fn into_py(self, py: Python<'_>) -> PyObject

Performs the conversion.

impl PyClass for PyStorageNodeClient

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for PyStorageNodeClient

const IS_BASETYPE: bool = false

#[pyclass(subclass)]

const IS_SUBCLASS: bool = false

#[pyclass(extends=…)]

const IS_MAPPING: bool = false

#[pyclass(mapping)]

const IS_SEQUENCE: bool = false

#[pyclass(sequence)]

type BaseType = PyAny

Base class

type ThreadChecker = SendablePyClass<PyStorageNodeClient>

This handles following two situations:

type PyClassMutability = <<PyAny as PyClassBaseType>::PyClassMutability as PyClassMutability>::MutableChild

Immutable or mutable

type Dict = PyClassDummySlot

Specify this class has #[pyclass(dict)] or not.

type WeakRef = PyClassDummySlot

Specify this class has #[pyclass(weakref)] or not.

type BaseNativeType = PyAny

The closest native ancestor. This is PyAny by default, and when you declare #[pyclass(extends=PyDict)], it’s PyDict.

fn items_iter() -> PyClassItemsIter

fn doc(py: Python<'_>) -> PyResult<&'static CStr>

Rendered class doc

fn lazy_type_object() -> &'static LazyTypeObject<Self>

fn dict_offset() -> Option<isize>

fn weaklist_offset() -> Option<isize>

impl<'a, 'py> PyFunctionArgument<'a, 'py> for &'a PyStorageNodeClient

type Holder = Option<PyRef<'py, PyStorageNodeClient>>

fn extract( obj: &'a Bound<'py, PyAny>, holder: &'a mut Self::Holder, ) -> PyResult<Self>

impl<'a, 'py> PyFunctionArgument<'a, 'py> for &'a mut PyStorageNodeClient

type Holder = Option<PyRefMut<'py, PyStorageNodeClient>>

fn extract( obj: &'a Bound<'py, PyAny>, holder: &'a mut Self::Holder, ) -> PyResult<Self>

impl PyMethods<PyStorageNodeClient> for PyClassImplCollector<PyStorageNodeClient>

fn py_methods(self) -> &'static PyClassItems

impl PyTypeInfo for PyStorageNodeClient

const NAME: &'static str = "StorageNodeClient"

Class name.

const MODULE: Option<&'static str> = ::core::option::Option::None

Module name, if any.

fn type_object_raw(py: Python<'_>) -> *mut PyTypeObject

Returns the PyTypeObject instance for this type.

fn type_object_bound(py: Python<'_>) -> Bound<'_, PyType>

Returns the safe abstraction over the type object.

fn is_type_of_bound(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type or a subclass of this type.

fn is_exact_type_of_bound(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type.

impl DerefToPyAny for PyStorageNodeClient