vastdb.table

VAST Database table.

class vastdb.table.Projection(name: str, table: Table, handle: int, stats: TableStats)

Bases: object

VAST semi-sorted projection.

property bucket

Return bucket.

columns() → pyarrow.Schema

Return this projections’ columns as an Arrow schema.

drop() → None

Drop this projection.

handle: int

name: str

rename(new_name) → None

Rename this projection.

property schema

Return schema.

stats: TableStats

table: Table

property tx

Return transaction.

class vastdb.table.SelectSplitState(query_data_request, table: Table, split_id: int, config: QueryConfig)

Bases: object

State of a specific query split execution.

property done

Returns true iff the pagination over.

process_split(api: VastdbApi, record_batches_queue: Queue[pyarrow.RecordBatch], check_stop: Callable)

Execute a sequence of QueryData requests, and queue the parsed RecordBatch objects.

Can be called repeatedly, to support resuming the query after a disconnection / retriable error.

class vastdb.table.Table(name: str, schema: Schema, handle: int, _imports_table: bool)

Bases: object

VAST Table.

add_column(new_column: pyarrow.Schema) → None

Add a new column.

arrow_schema: pyarrow.Schema

property bucket

Return bucket.

columns() → pyarrow.Schema

Return columns’ metadata.

create_imports_table(fail_if_exists=True) → Table

Create imports table.

create_projection(projection_name: str, sorted_columns: List[str], unsorted_columns: List[str]) → Projection

Create a new semi-sorted projection.

delete(rows: pyarrow.RecordBatch | pyarrow.Table) → None

Delete a subset of rows in this table.

Row IDs are specified using a special field (named “$row_id” of uint64 type).

drop() → None

Drop this table.

drop_column(column_to_drop: pyarrow.Schema) → None

Drop an existing column.

get_stats() → TableStats

Get the statistics of this table.

handle: int

import_files(files_to_import: Iterable[str], config: ImportConfig | None = None) → None

Import a list of Parquet files into this table.

The files must be on VAST S3 server and be accessible using current credentials.

import_partitioned_files(files_and_partitions: Dict[str, pyarrow.RecordBatch], config: ImportConfig | None = None) → None

Import a list of Parquet files into this table.

The files must be on VAST S3 server and be accessible using current credentials. Each file must have its own partition values defined as an Arrow RecordBatch.

imports_table() → Table | None

Get the imports table of this table.

insert(rows: pyarrow.RecordBatch | pyarrow.Table)

Insert a RecordBatch into this table.

insert_in_column_batches(rows: pyarrow.RecordBatch)

Split the RecordBatch into max_columns that can be inserted in single RPC.

Insert first MAX_COLUMN_IN_BATCH columns and get the row_ids. Then loop on the rest of the columns and update in groups of MAX_COLUMN_IN_BATCH.

name: str

property path

Return table’s path.

projection(name: str) → Projection

Get a specific semi-sorted projection of this table.

projections(projection_name=None) → Iterable[Projection]

List all semi-sorted projections of this table.

rename(new_name) → None

Rename this table.

rename_column(current_column_name: str, new_column_name: str) → None

Rename an existing column.

schema: Schema

select(columns: List[str] | None = None, predicate: ibis.expr.types.BooleanColumn | ibis.common.deferred.Deferred = None, config: QueryConfig | None = None, *, internal_row_id: bool = False) → pyarrow.RecordBatchReader

Execute a query over this table.

To read a subset of the columns, specify their names via columns argument. Otherwise, all columns will be read.

In order to apply a filter, a predicate can be specified. See https://github.com/vast-data/vastdb_sdk/blob/main/README.md#filters-and-projections for more details.

Query-execution configuration options can be specified via the optional config argument.

property stats

Fetch table’s statistics from server.

property tx

Return transaction.

update(rows: pyarrow.RecordBatch | pyarrow.Table, columns: List[str] | None = None) → None

Update a subset of cells in this table.

Row IDs are specified using a special field (named “$row_id” of uint64 type) - this function assume that this special field is part of arguments.

A subset of columns to be updated can be specified via the columns argument.

class vastdb.table.TableStats(num_rows: int, size_in_bytes: int, is_external_rowid_alloc: bool = False, endpoints: Tuple[str, ...] = ())

Bases: object

Table-related information.

endpoints: Tuple[str, ...] = ()

is_external_rowid_alloc: bool = False

num_rows: int

size_in_bytes: int