ajamato commented on a change in pull request #13017:
URL: https://github.com/apache/beam/pull/13017#discussion_r501954641
##########
File path: sdks/python/apache_beam/io/gcp/bigquery_tools.py
##########
@@ -271,6 +276,11 @@ def __init__(self, client=None):
# randomized prefix for row IDs.
self._row_id_prefix = '' if client else uuid.uuid4()
self._temporary_table_suffix = uuid.uuid4().hex
+ self._latency_histogram_metric = Metrics.histogram(
Review comment:
We do plan on building a few system metrics for GCP IO API request call
latency which use histograms. Though, it doesn't need to use the user metric
framework itself, its a reasonable way to implement it to use a lot of the same
primitives.
I asked @ihji to use the metric framework for the logger, as I will be
adding a latency histogram metrics for these BigQuery API request calls.
##########
File path: sdks/python/apache_beam/io/gcp/bigquery_tools.py
##########
@@ -271,6 +276,11 @@ def __init__(self, client=None):
# randomized prefix for row IDs.
self._row_id_prefix = '' if client else uuid.uuid4()
self._temporary_table_suffix = uuid.uuid4().hex
+ self._latency_histogram_metric = Metrics.histogram(
Review comment:
We do plan on building a few system metrics for GCP IO API request call
latency which use histograms. Though, it doesn't need to use the user metric
framework itself, its a reasonable way to implement it to use a lot of the same
primitives.
I asked @ihji to use the metric framework for the logger, as I will be
adding a latency histogram metrics for these BigQuery API request calls. The
previous PR has written basically a side framework to count the API call
latencies into a histogram and log them. But since I will be introducing a
metric anyways, I suggested using this approach.
##########
File path: sdks/python/apache_beam/io/gcp/bigquery_tools.py
##########
@@ -271,6 +276,11 @@ def __init__(self, client=None):
# randomized prefix for row IDs.
self._row_id_prefix = '' if client else uuid.uuid4()
self._temporary_table_suffix = uuid.uuid4().hex
+ self._latency_histogram_metric = Metrics.histogram(
Review comment:
We do plan on building a few system metrics for GCP IO API request call
latency which use histograms. Though, it doesn't need to use the user metric
framework itself, its a reasonable way to implement it to use a lot of the same
primitives.
I asked @ihji to use the metric framework for the logger, as I will be
adding a latency histogram metrics for these BigQuery API request calls. The
previous PR has written basically a side framework to count the API call
latencies into a histogram and log them. But since I will be introducing a
metric anyways, I suggested using this approach. So we wouldn't essentially
have two code paths to count the metric for logging, and for the system metric.
##########
File path: sdks/python/apache_beam/io/gcp/bigquery_tools.py
##########
@@ -271,6 +276,11 @@ def __init__(self, client=None):
# randomized prefix for row IDs.
self._row_id_prefix = '' if client else uuid.uuid4()
self._temporary_table_suffix = uuid.uuid4().hex
+ self._latency_histogram_metric = Metrics.histogram(
Review comment:
We do plan on building a few system metrics for GCP IO API request call
latency which use histograms. Though, it doesn't need to use the user metric
framework itself, its a reasonable way to implement it to use a lot of the same
primitives.
I asked @ihji to use the metric framework for the logger, as I will be
adding a latency histogram metrics for these BigQuery API request calls. The
previous PR has written basically a side framework to count the API call
latencies into a histogram and log them. But since I will be introducing a
metric anyways, I suggested using this approach. So we wouldn't essentially
have two code paths to count the latency values for logging, and for the system
metric.
##########
File path: sdks/python/apache_beam/io/gcp/bigquery_tools.py
##########
@@ -271,6 +276,11 @@ def __init__(self, client=None):
# randomized prefix for row IDs.
self._row_id_prefix = '' if client else uuid.uuid4()
self._temporary_table_suffix = uuid.uuid4().hex
+ self._latency_histogram_metric = Metrics.histogram(
Review comment:
We do plan on building a few system metrics for GCP IO API request call
latency which use histograms. Though, it doesn't need to use the user metric
framework itself, its a reasonable way to implement it to use a lot of the same
techniques.
I asked @ihji to use the metric framework for the logger, as I will be
adding a latency histogram metrics for these BigQuery API request calls. The
previous PR has written basically a side framework to count the API call
latencies into a histogram and log them. But since I will be introducing a
metric anyways, I suggested using this approach. So we wouldn't essentially
have two code paths to count the latency values for logging, and for the system
metric.
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