Data API
After creating a Table
and some
column families, you are ready to store and retrieve data.
Cells vs. Columns vs. Column Families
As explained in the 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 columnsbar
andbaz
. These would typically be represented asfoo:bar
andfoo: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
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
DirectRow
class handles direct mutations.The conditional way is via CheckAndMutateRow. This method first checks if some filter is matched in 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
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
AppendRow
class handles append mutations.
Row Factory
A single factory can be used to create any of the three row types.
To create a DirectRow
:
row = table.row(row_key)
Unlike the previous string values we’ve used before, the row key must
be bytes
.
To create a ConditionalRow
,
first create a RowFilter
and
then
cond_row = table.row(row_key, filter_=filter_)
To create an AppendRow
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 off in a batch via
row.commit()
Direct Mutations
Direct mutations can be added via one of four methods
set_cell()
allows a single value to be written to a column
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 be bytes or an integer, which will be converted to bytes as a signed 64-bit integer.
delete_cell()
deletes all cells (i.e. for all timestamps) in a given column
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
TimestampRange
can
be used
row.delete_cell(column_family_id, column,
time_range=time_range)
delete_cells()
does the same thing asdelete_cell()
, but accepts a list of columns in a column family rather than a single one.
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 ALL_COLUMNS
value can be used
row.delete_cells(column_family_id, row.ALL_COLUMNS,
time_range=time_range)
delete()
will delete the entire row
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:
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
append_cell_value()
appends a bytes value to an existing cell:
append_row.append_cell_value(column_family_id, column, bytes_value)
increment_cell_value()
increments an integer value in an existing cell:
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
row.clear()
Reading Data
Read Single Row from a Table
To make a ReadRows API request for a single row key, use
Table.read_row()
:
>>> row_data = table.read_row(row_key)
>>> row_data.cells
{
u'fam1': {
b'col1': [
<google.cloud.bigtable.row_data.Cell at 0x7f80d150ef10>,
<google.cloud.bigtable.row_data.Cell at 0x7f80d150ef10>,
],
b'col2': [
<google.cloud.bigtable.row_data.Cell at 0x7f80d150ef10>,
],
},
u'fam2': {
b'col3': [
<google.cloud.bigtable.row_data.Cell at 0x7f80d150ef10>,
<google.cloud.bigtable.row_data.Cell at 0x7f80d150ef10>,
<google.cloud.bigtable.row_data.Cell at 0x7f80d150ef10>,
],
},
}
>>> cell = row_data.cells[u'fam1'][b'col1'][0]
>>> cell
<google.cloud.bigtable.row_data.Cell at 0x7f80d150ef10>
>>> cell.value
b'val1'
>>> cell.timestamp
datetime.datetime(2016, 2, 27, 3, 41, 18, 122823, tzinfo=<UTC>)
Rather than returning a DirectRow
or similar class, this method returns a
PartialRowData
instance. This class is used for reading and parsing data rather than for
modifying data (as DirectRow
is).
A filter can also be applied to the results:
row_data = table.read_row(row_key, filter_=filter_val)
The allowable filter_
values are the same as those used for a
ConditionalRow
. For
more information, see the
Table.read_row()
documentation.
Stream Many Rows from a Table
To make a ReadRows API request for a stream of rows, use
Table.read_rows()
:
row_data = table.read_rows()
Using gRPC over HTTP/2, a continual stream of responses will be delivered. In particular
consume_next()
pulls the next result from the stream, parses it and stores it on thePartialRowsData
instanceconsume_all()
pulls results from the stream until there are no morecancel()
closes the stream
See the PartialRowsData
documentation for more information.
As with
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 Table.read_rows()
documentation for more information on the optional arguments.
Sample Keys in a Table
Make a SampleRowKeys API request with
Table.sample_row_keys()
:
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
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
next_key = keys_iterator.next()
or all keys can be iterated over via
for curr_key in keys_iterator:
do_something(curr_key)
Just as with reading, the stream can be canceled:
keys_iterator.cancel()