Data API ======== After creating a :class:`Table ` and some column families, you are ready to store and retrieve data. Cells vs. Columns vs. Column Families +++++++++++++++++++++++++++++++++++++ * As explained in the :doc:`table overview `, tables can have many column families. * As described below, a table can also have many rows which are specified by row keys. * Within a row, data is stored in a cell. A cell simply has a value (as bytes) and a timestamp. The number of cells in each row can be different, depending on what was stored in each row. * Each cell lies in a column (**not** a column family). A column is really just a more **specific** modifier within a column family. A column can be present in every column family, in only one or anywhere in between. * Within a column family there can be many columns. For example within the column family ``foo`` we could have columns ``bar`` and ``baz``. These would typically be represented as ``foo:bar`` and ``foo:baz``. Modifying Data ++++++++++++++ Since data is stored in cells, which are stored in rows, we use the metaphor of a **row** in classes that are used to modify (write, update, delete) data in a :class:`Table `. Direct vs. Conditional vs. Append --------------------------------- There are three ways to modify data in a table, described by the `MutateRow`_, `CheckAndMutateRow`_ and `ReadModifyWriteRow`_ API methods. * The **direct** way is via `MutateRow`_ which involves simply adding, overwriting or deleting cells. The :class:`DirectRow ` class handles direct mutations. * The **conditional** way is via `CheckAndMutateRow`_. This method first checks if some filter is matched in a a given row, then applies one of two sets of mutations, depending on if a match occurred or not. (These mutation sets are called the "true mutations" and "false mutations".) The :class:`ConditionalRow ` class handles conditional mutations. * The **append** way is via `ReadModifyWriteRow`_. This simply appends (as bytes) or increments (as an integer) data in a presumed existing cell in a row. The :class:`AppendRow ` class handles append mutations. Row Factory ----------- A single factory can be used to create any of the three row types. To create a :class:`DirectRow `: .. code:: python row = table.row(row_key) Unlike the previous string values we've used before, the row key must be ``bytes``. To create a :class:`ConditionalRow `, first create a :class:`RowFilter ` and then .. code:: python cond_row = table.row(row_key, filter_=filter_) To create an :class:`AppendRow ` .. code:: python append_row = table.row(row_key, append=True) Building Up Mutations --------------------- In all three cases, a set of mutations (or two sets) are built up on a row before they are sent of in a batch via .. code:: python row.commit() Direct Mutations ---------------- Direct mutations can be added via one of four methods * :meth:`set_cell() ` allows a single value to be written to a column .. code:: python row.set_cell(column_family_id, column, value, timestamp=timestamp) If the ``timestamp`` is omitted, the current time on the Google Cloud Bigtable server will be used when the cell is stored. The value can either by bytes or an integer (which will be converted to bytes as a signed 64-bit integer). * :meth:`delete_cell() ` deletes all cells (i.e. for all timestamps) in a given column .. code:: python row.delete_cell(column_family_id, column) Remember, this only happens in the ``row`` we are using. If we only want to delete cells from a limited range of time, a :class:`TimestampRange ` can be used .. code:: python row.delete_cell(column_family_id, column, time_range=time_range) * :meth:`delete_cells() ` does the same thing as :meth:`delete_cell() ` but accepts a list of columns in a column family rather than a single one. .. code:: python row.delete_cells(column_family_id, [column1, column2], time_range=time_range) In addition, if we want to delete cells from every column in a column family, the special :attr:`ALL_COLUMNS ` value can be used .. code:: python row.delete_cells(column_family_id, row.ALL_COLUMNS, time_range=time_range) * :meth:`delete() ` will delete the entire row .. code:: python row.delete() Conditional Mutations --------------------- Making **conditional** modifications is essentially identical to **direct** modifications: it uses the exact same methods to accumulate mutations. However, each mutation added must specify a ``state``: will the mutation be applied if the filter matches or if it fails to match. For example: .. code:: python cond_row.set_cell(column_family_id, column, value, timestamp=timestamp, state=True) will add to the set of true mutations. Append Mutations ---------------- Append mutations can be added via one of two methods * :meth:`append_cell_value() ` appends a bytes value to an existing cell: .. code:: python append_row.append_cell_value(column_family_id, column, bytes_value) * :meth:`increment_cell_value() ` increments an integer value in an existing cell: .. code:: python append_row.increment_cell_value(column_family_id, column, int_value) Since only bytes are stored in a cell, the cell value is decoded as a signed 64-bit integer before being incremented. (This happens on the Google Cloud Bigtable server, not in the library.) Notice that no timestamp was specified. This is because **append** mutations operate on the latest value of the specified column. If there are no cells in the specified column, then the empty string (bytes case) or zero (integer case) are the assumed values. Starting Fresh -------------- If accumulated mutations need to be dropped, use .. code:: python row.clear() Reading Data ++++++++++++ Read Single Row from a Table ---------------------------- To make a `ReadRows`_ API request for a single row key, use :meth:`Table.read_row() `: .. code:: python >>> row_data = table.read_row(row_key) >>> row_data.cells { u'fam1': { b'col1': [ , , ], b'col2': [ , ], }, u'fam2': { b'col3': [ , , , ], }, } >>> cell = row_data.cells[u'fam1'][b'col1'][0] >>> cell >>> cell.value b'val1' >>> cell.timestamp datetime.datetime(2016, 2, 27, 3, 41, 18, 122823, tzinfo=) Rather than returning a :class:`DirectRow ` or similar class, this method returns a :class:`PartialRowData ` instance. This class is used for reading and parsing data rather than for modifying data (as :class:`DirectRow ` is). A filter can also be applied to the results: .. code:: python row_data = table.read_row(row_key, filter_=filter_val) The allowable ``filter_`` values are the same as those used for a :class:`ConditionalRow `. For more information, see the :meth:`Table.read_row() ` documentation. Stream Many Rows from a Table ----------------------------- To make a `ReadRows`_ API request for a stream of rows, use :meth:`Table.read_rows() `: .. code:: python row_data = table.read_rows() Using gRPC over HTTP/2, a continual stream of responses will be delivered. In particular * :meth:`consume_next() ` pulls the next result from the stream, parses it and stores it on the :class:`PartialRowsData ` instance * :meth:`consume_all() ` pulls results from the stream until there are no more * :meth:`cancel() ` closes the stream See the :class:`PartialRowsData ` documentation for more information. As with :meth:`Table.read_row() `, an optional ``filter_`` can be applied. In addition a ``start_key`` and / or ``end_key`` can be supplied for the stream, a ``limit`` can be set and a boolean ``allow_row_interleaving`` can be specified to allow faster streamed results at the potential cost of non-sequential reads. See the :meth:`Table.read_rows() ` documentation for more information on the optional arguments. Sample Keys in a Table ---------------------- Make a `SampleRowKeys`_ API request with :meth:`Table.sample_row_keys() `: .. code:: python keys_iterator = table.sample_row_keys() The returned row keys will delimit contiguous sections of the table of approximately equal size, which can be used to break up the data for distributed tasks like mapreduces. As with :meth:`Table.read_rows() `, the returned ``keys_iterator`` is connected to a cancellable HTTP/2 stream. The next key in the result can be accessed via .. code:: python next_key = keys_iterator.next() or all keys can be iterated over via .. code:: python for curr_key in keys_iterator: do_something(curr_key) Just as with reading, the stream can be canceled: .. code:: python keys_iterator.cancel() .. _ReadRows: https://github.com/GoogleCloudPlatform/cloud-bigtable-client/blob/2aae624081f652427052fb652d3ae43d8ac5bf5a/bigtable-protos/src/main/proto/google/bigtable/v1/bigtable_service.proto#L36-L38 .. _SampleRowKeys: https://github.com/GoogleCloudPlatform/cloud-bigtable-client/blob/2aae624081f652427052fb652d3ae43d8ac5bf5a/bigtable-protos/src/main/proto/google/bigtable/v1/bigtable_service.proto#L44-L46 .. _MutateRow: https://github.com/GoogleCloudPlatform/cloud-bigtable-client/blob/2aae624081f652427052fb652d3ae43d8ac5bf5a/bigtable-protos/src/main/proto/google/bigtable/v1/bigtable_service.proto#L50-L52 .. _CheckAndMutateRow: https://github.com/GoogleCloudPlatform/cloud-bigtable-client/blob/2aae624081f652427052fb652d3ae43d8ac5bf5a/bigtable-protos/src/main/proto/google/bigtable/v1/bigtable_service.proto#L62-L64 .. _ReadModifyWriteRow: https://github.com/GoogleCloudPlatform/cloud-bigtable-client/blob/2aae624081f652427052fb652d3ae43d8ac5bf5a/bigtable-protos/src/main/proto/google/bigtable/v1/bigtable_service.proto#L70-L72