def after_execute(conn, elt, multiparams, params, result):
duration = 1000 * (time.time() - conn.info['query_start_time'].pop(-1))
action = elt.__class__.__name__
if action == 'Select':
name = 'unknown'
try:
name = _table_name_from_select_element(elt)
except Exception:
logging.exception('Failed finding table name.')
elif action in ['Update', 'Insert', 'Delete']:
name = elt.table.name
else:
# create/drop tables, sqlalchemy internal schema queries, etc
return
action = action.lower()
statsd_client.timing('db.{}.{}'.format(name, action), duration)
metrics_logger.debug("table=%s query=%s duration=%.2f", name, action,
duration)
if has_request_context():
g.setdefault('queries_count', 0)
g.setdefault('queries_duration', 0)
g.queries_count += 1
g.queries_duration += duration
return result
def after_execute(conn, elt, multiparams, params, result):
duration = 1000 * (time.time() - conn.info['query_start_time'].pop(-1))
action = elt.__class__.__name__
if action == 'Select':
t = elt.froms[0]
while isinstance(t, _ORMJoin):
t = t.left
name = t.name
elif action in ['Update', 'Insert', 'Delete']:
name = elt.table.name
else:
# create/drop tables, sqlalchemy internal schema queries, etc
return
action = action.lower()
statsd_client.timing('db.{}.{}'.format(name, action), duration)
metrics_logger.debug("table=%s query=%s duration=%.2f", name, action,
duration)
if has_request_context():
g.setdefault('queries_count', 0)
g.setdefault('queries_duration', 0)
g.queries_count += 1
g.queries_duration += duration
return result
def task_postrun_handler(signal, sender, task_id, task, args, kwargs, retval,
state, **kw):
try:
run_time = 1000 * (time.time() - tasks_start_time.pop(task_id))
state = (state or "unknown").lower()
tags = {"state": state, "hostname": socket.gethostname()}
if task.name == "redash.tasks.execute_query":
if isinstance(retval, Exception):
tags["state"] = "exception"
state = "exception"
tags["data_source_id"] = args[1]
normalized_task_name = task.name.replace("redash.tasks.",
"").replace(".", "_")
metric = "celery.task_runtime.{}".format(normalized_task_name)
logging.debug(
"metric=%s",
json_dumps({
"metric": metric,
"tags": tags,
"value": run_time
}))
statsd_client.timing(metric_name(metric, tags), run_time)
statsd_client.incr(
metric_name(
"celery.task.{}.{}".format(normalized_task_name, state), tags))
except Exception:
logging.exception("Exception during task_postrun handler.")
def after_execute(conn, elt, multiparams, params, result):
duration = 1000 * (time.time() - conn.info['query_start_time'].pop(-1))
action = elt.__class__.__name__
if action == 'Select':
name = 'unknown'
try:
name = _table_name_from_select_element(elt)
except Exception:
logging.exception('Failed finding table name.')
elif action in ['Update', 'Insert', 'Delete']:
name = elt.table.name
else:
# create/drop tables, sqlalchemy internal schema queries, etc
return
action = action.lower()
statsd_client.timing('db.{}.{}'.format(name, action), duration)
metrics_logger.debug("table=%s query=%s duration=%.2f", name, action,
duration)
if has_request_context():
g.setdefault('queries_count', 0)
g.setdefault('queries_duration', 0)
g.queries_count += 1
g.queries_duration += duration
return result
def calculate_metrics(response):
if "start_time" not in g:
return response
request_duration = (time.time() - g.start_time) * 1000
queries_duration = g.get("queries_duration", 0.0)
queries_count = g.get("queries_count", 0.0)
endpoint = (request.endpoint or "unknown").replace(".", "_")
metrics_logger.info(
"method=%s path=%s endpoint=%s status=%d content_type=%s content_length=%d duration=%.2f query_count=%d query_duration=%.2f",
request.method,
request.path,
endpoint,
response.status_code,
response.content_type,
response.content_length or -1,
request_duration,
queries_count,
queries_duration,
)
statsd_client.timing(
"requests.{}.{}".format(endpoint, request.method.lower()),
request_duration)
return response
def task_postrun_handler(signal, sender, task_id, task, args, kwargs, retval,
state, **kw):
try:
run_time = 1000 * (time.time() - tasks_start_time.pop(task_id))
state = (state or 'unknown').lower()
tags = {'state': state, 'hostname': socket.gethostname()}
if task.name == 'redash.tasks.execute_query':
if isinstance(retval, Exception):
tags['state'] = 'exception'
state = 'exception'
tags['data_source_id'] = args[1]
normalized_task_name = task.name.replace('redash.tasks.',
'').replace('.', '_')
metric = "celery.task_runtime.{}".format(normalized_task_name)
logging.debug(
"metric=%s",
json_dumps({
'metric': metric,
'tags': tags,
'value': run_time
}))
statsd_client.timing(metric_name(metric, tags), run_time)
statsd_client.incr(
metric_name(
'celery.task.{}.{}'.format(normalized_task_name, state), tags))
except Exception:
logging.exception("Exception during task_postrun handler.")
def task_postrun_handler(signal, sender, task_id, task, args, kwargs, retval,
state):
try:
run_time = 1000 * (time.time() - tasks_start_time.pop(task_id))
tags = {
'name': task.name,
'state': (state or 'unknown').lower(),
'hostname': socket.gethostname()
}
if task.name == 'redash.tasks.execute_query':
if isinstance(retval, Exception):
tags['state'] = 'exception'
tags['data_source_id'] = args[1]
metric = "celery.task.runtime"
logging.debug(
"metric=%s",
json.dumps({
'metric': metric,
'tags': tags,
'value': run_time
}))
statsd_client.timing(metric_name(metric, tags), run_time)
statsd_client.incr(metric_name('celery.task.count', tags))
except Exception:
logging.exception("Exception during task_postrun handler.")
def _execute_and_measure(cls, action, args, kwargs):
name = cls.__name__
start_time = time.time()
try:
result = getattr(super(MeteredModel, cls), action)(*args, **kwargs)
return result
finally:
duration = (time.time() - start_time)*1000
statsd_client.timing('db.{}.{}'.format(name, action), duration)
metrics_logger.debug("model=%s query=%s duration=%.2f", name, action, duration)
def metered_execute(self, *args, **kwargs):
name = self.model_class.__name__
action = getattr(self, 'model_action', 'unknown')
start_time = time.time()
try:
result = real_execute(self, *args, **kwargs)
return result
finally:
duration = (time.time() - start_time) * 1000
statsd_client.timing('db.{}.{}'.format(name, action), duration)
metrics_logger.debug("model=%s query=%s duration=%.2f", name, action, duration)
def metered_execute(self, *args, **kwargs):
name = self.model_class.__name__
action = getattr(self, 'model_action', 'unknown')
start_time = time.time()
try:
result = real_execute(self, *args, **kwargs)
return result
finally:
duration = (time.time() - start_time) * 1000
statsd_client.timing('db.{}.{}'.format(name, action), duration)
metrics_logger.debug("model=%s query=%s duration=%.2f", name,
action, duration)
def calculate_metrics(response):
request_duration = (time.time() - g.start_time) * 1000
metrics_logger.info("method=%s path=%s endpoint=%s status=%d content_type=%s content_length=%d duration=%.2f query_count=%d query_duration=%.2f",
request.method,
request.path,
request.endpoint,
response.status_code,
response.content_type,
response.content_length,
request_duration,
db.database.query_count,
db.database.query_duration)
statsd_client.timing('requests.{}.{}'.format(request.endpoint, request.method.lower()), request_duration)
return response
def task_postrun_handler(signal, sender, task_id, task, args, kwargs, retval, state):
try:
run_time = 1000 * (time.time() - tasks_start_time.pop(task_id))
tags = {"name": task.name, "state": (state or "unknown").lower(), "hostname": socket.gethostname()}
if task.name == "redash.tasks.execute_query":
if isinstance(retval, Exception):
tags["state"] = "exception"
tags["data_source_id"] = args[1]
metric = "celery.task.runtime"
logging.debug("metric=%s", json.dumps({"metric": metric, "tags": tags, "value": run_time}))
statsd_client.timing(metric_name(metric, tags), run_time)
statsd_client.incr(metric_name("celery.task.count", tags))
except Exception:
logging.exception("Exception during task_postrun handler.")
def after_execute(conn, elt, multiparams, params, result):
# pop 移除指定index的元素
# remove 移除指元素中第一个
duration = 1000 * (time.time() - conn.info['query_start_time'].pop(-1))
action = elt.__class__.__name__
if action == 'Select':
name = 'unknown'
try:
name = _table_name_from_select_element(elt)
except Exception:
logging.exception('Failed finding table name.')
elif action in ['Update', 'Insert', 'Delete']:
name = elt.table.name
else:
# create/drop tables, sqlalchemy internal schema queries, etc
return
action = action.lower()
# 对某个表的，执行了的什么操作（select,update等），消耗多长时间
statsd_client.timing('db.{}.{}'.format(name, action), duration)
# 日志记录
metrics_logger.debug("table=%s query=%s duration=%.2f", name, action,
duration)
if has_request_context():
# current_app、g就是应用上下文, 线程成内的全局变量
# requests、session就是请求上下文
# request 封装了client发出的http请求内容
# session 储存了用户请求之间的需要记住的值字典
# 源码你就会发现 session 也是一个 request context 的变量，但它把数据保存到了 cookie 中并发送到了客户端，客户端再次请求的时候又带上了cookie。
g.setdefault('queries_count', 0)
g.setdefault('queries_duration', 0)
g.queries_count += 1
g.queries_duration += duration
return result
def calculate_metrics(response):
if 'start_time' not in g:
return response
request_duration = (time.time() - g.start_time) * 1000
queries_duration = g.get('queries_duration', 0.0)
queries_count = g.get('queries_count', 0.0)
metrics_logger.info(
"method=%s path=%s endpoint=%s status=%d content_type=%s content_length=%d duration=%.2f query_count=%d query_duration=%.2f",
request.method, request.path, request.endpoint, response.status_code,
response.content_type, response.content_length, request_duration,
queries_count, queries_duration)
statsd_client.timing(
'requests.{}.{}'.format(request.endpoint, request.method.lower()),
request_duration)
return response
def task_postrun_handler(signal, sender, task_id, task, args, kwargs, retval, state, **kw):
try:
run_time = 1000 * (time.time() - tasks_start_time.pop(task_id))
state = (state or 'unknown').lower()
tags = {'state': state, 'hostname': socket.gethostname()}
if task.name == 'redash.tasks.execute_query':
if isinstance(retval, Exception):
tags['state'] = 'exception'
state = 'exception'
tags['data_source_id'] = args[1]
normalized_task_name = task.name.replace('redash.tasks.', '').replace('.', '_')
metric = "celery.task_runtime.{}".format(normalized_task_name)
logging.debug("metric=%s", json_dumps({'metric': metric, 'tags': tags, 'value': run_time}))
statsd_client.timing(metric_name(metric, tags), run_time)
statsd_client.incr(metric_name('celery.task.{}.{}'.format(normalized_task_name, state), tags))
except Exception:
logging.exception("Exception during task_postrun handler.")
def calculate_metrics(response):
if 'start_time' not in g:
####返还给客户端
return response
####计算整个请求消耗时间########
request_duration = (time.time() - g.start_time) * 1000
####计算此次请求的数据库消耗时间以及频率########
# D:\redash - master\redash\metrics\database.py
# if has_request_context():
# g.setdefault('queries_count', 0)
# g.setdefault('queries_duration', 0)
# g.queries_count += 1
# g.queries_duration += duration
queries_duration = g.get('queries_duration', 0.0)
queries_count = g.get('queries_count', 0.0)
###########记录请求资源##############
endpoint = (request.endpoint or 'unknown').replace('.', '_')
###########记录到日志里#############
metrics_logger.info(
"method=%s path=%s endpoint=%s status=%d content_type=%s content_length=%d duration=%.2f query_count=%d query_duration=%.2f",
request.method, request.path, endpoint, response.status_code,
response.content_type, response.content_length or -1, request_duration,
queries_count, queries_duration)
####### 利用statsd可以记录request和time的关系，制作一个时间流的图形
####### statsd可以采集指标，指标就是随时间变化度量的值
###### 事实上，logging也可以通过time和记录，通过grep统计出来，但是太麻烦了？？？？？
statsd_client.timing(
'requests.{}.{}'.format(endpoint, request.method.lower()),
request_duration)
####返还给客户端
return response
def calculate_metrics(response):
if 'start_time' not in g:
return response
request_duration = (time.time() - g.start_time) * 1000
queries_duration = g.get('queries_duration', 0.0)
queries_count = g.get('queries_count', 0.0)
metrics_logger.info("method=%s path=%s endpoint=%s status=%d content_type=%s content_length=%d duration=%.2f query_count=%d query_duration=%.2f",
request.method,
request.path,
request.endpoint,
response.status_code,
response.content_type,
response.content_length,
request_duration,
queries_count,
queries_duration)
statsd_client.timing('requests.{}.{}'.format(request.endpoint, request.method.lower()), request_duration)
return response
