If you do not have requirements on the order of query results, you can use parallel scan to quickly obtain query results.
Prerequisites
- The OTSClient is initialized. For more information, see Initialization.
- A data table is created. Data is written to the table.
- A search index is created for the data table. For more information, see Create search indexes.
Parameters
Parameter | Description | |
---|---|---|
table_name | The name of the data table. | |
index_name | The name of the search index. | |
scan_query | query | The query statement for the search index. The operation supports term query, fuzzy query, range query, geo query, and nested query, which are similar to those of the Search operation. |
limit | The maximum number of rows that can be returned by each ParallelScan call. | |
max_parallel | The maximum number of parallel scan tasks per request. The maximum number of parallel scan tasks per request varies based on the data volume. A larger volume of data requires more parallel scan tasks per request. You can use the ComputeSplits operation to query the maximum number of parallel scan tasks per request. | |
current_parallel_id | The ID of the parallel scan task in the request. Valid values: [0, max_parallel). | |
token | The token that is used to paginate query results. The results of the ParallelScan request contain the token for the next page. You can use the token to retrieve the next page. | |
alive_time | The validity period of the current parallel scan task. This validity period is also
the validity period of the token. Unit: seconds. Default value: 60. We recommend that
you use the default value. If the next request is not initiated within the validity
period, more data cannot be queried. The validity time of the token is refreshed each
time you send a request.
Note The server uses the asynchronous method to process expired tasks. The current task
does not expire within the validity period. However, Tablestore does not guarantee
that the task expires after the validity period.
|
|
columns_to_get | You can use parallel scan to scan data only in search indexes. To use parallel scan for a search index, you must set store to true when you create the search index. | |
session_id | The session ID of the parallel scan task. You can call the ComputeSplits operation to create a session and query the maximum number of parallel scan tasks that are supported by the parallel scan request. |
Examples
def fetch_rows_per_thread(query, session_id, current_thread_id, max_thread_num):
token = None
while True:
try:
scan_query = ScanQuery(query, limit = 20, next_token = token, current_parallel_id = current_thread_id,
max_parallel = max_thread_num, alive_time = 30)
response = client.parallel_scan(
table_name, index_name, scan_query, session_id,
columns_to_get = ColumnsToGet(return_type=ColumnReturnType.ALL_FROM_INDEX))
for row in response.rows:
print("%s:%s" % (threading.currentThread().name, str(row)))
if len(response.next_token) == 0:
break
else:
token = response.next_token
except OTSServiceError as e:
print (e)
except OTSClientError as e:
print (e)
def parallel_scan(table_name, index_name):
response = client.compute_splits(table_name, index_name)
query = TermQuery('d', 0.1)
params = []
for i in range(response.splits_size):
params.append((([query, response.session_id, i, response.splits_size], None)))
pool = threadpool.ThreadPool(response.splits_size)
requests = threadpool.makeRequests(fetch_rows_per_thread, params)
[pool.putRequest(req) for req in requests]
pool.wait()