Feature/processor output individual records for gratia, add CPU usage and memory metrics

Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.12
+ARG UID=10000
+ARG GID=10000
+
+WORKDIR /src
+COPY python/requirements.txt .
+RUN pip install -r requirements.txt
+COPY python/*.py ./
+
+USER $UID:$GID
+CMD python3 KAPEL.py

docker-compose.yml

@@ -0,0 +1,6 @@
+services:
+  kapel-processor:
+    image: hub.opensciencegrid.org/osgpreview/kapel-processor:latest
+    build:
+      context: .
+      network: host

python/KAPEL.py

@@ -56,6 +56,11 @@ def __init__(self, queryRange, namespace):
         self.endtime = f'max_over_time(kube_pod_completion_time{{namespace="{namespace}"}}[{queryRange}])'
         self.starttime = f'max_over_time(kube_pod_start_time{{namespace="{namespace}"}}[{queryRange}])'
         self.cores = f'max_over_time(kube_pod_container_resource_requests{{resource="cpu", node != "", namespace="{namespace}"}}[{queryRange}])'
+        self.memory = f'sum by (pod) (max_over_time(kube_pod_container_resource_requests{{resource="memory", node!="", namespace="{namespace}"}}[{queryRange}])) / 1000'
+
+        # This is container-level CPU usage reported by kubelets, for gratia output.
+        # Take the largest (i.e. final) value of the cumulative CPU usage of each container, and sum the results for all containers in a pod.
+        self.cpuusage = f'sum by (pod) (last_over_time(container_cpu_usage_seconds_total{{namespace="{namespace}"}}[{queryRange}]))'
 
 def summary_message(config, year, month, wall_time, cpu_time, n_jobs, first_end, last_end):
     output = (
@@ -81,6 +86,29 @@ def summary_message(config, year, month, wall_time, cpu_time, n_jobs, first_end,
     )
     return output
 
+def individual_message(config, pod_name, memory, cores, wall_time, cpu_time, start_time, end_time):
+    """ Write an APEL individual job message based on prometheus metrics from a single pod """
+    output = (
+        f'APEL-individual-job-message: v0.3\n'
+        f'Site: {config.site_name}\n'
+        f'VO: {config.vo_name}\n'
+        f'SubmitHost: {config.submit_host}\n'
+        f'MachineName: {pod_name}\n'
+        f'LocalJobId: {pod_name}\n'
+        f'InfrastructureType: {config.infrastructure_type}\n'
+        f'ServiceLevelType: si2k\n'
+        f'ServiceLevel: {config.benchmark_value * 250}\n'
+        f'WallDuration: {wall_time}\n'
+        f'CpuDuration: {cpu_time}\n'
+        f'MemoryVirtual: {memory}\n'
+        f'Processors: {cores}\n'
+        f'NodeCount: {config.nodecount}\n'
+        f'StartTime: {start_time}\n'
+        f'EndTime: {end_time}\n'
+        f'%%\n'
+    )
+    return output
+
 def sync_message(config, year, month, n_jobs):
     output = (
         f'APEL-sync-message: v0.1\n'
@@ -159,43 +187,14 @@ def rearrange(x):
         # this produces each of the (key, value) tuples in the list
         yield item['metric']['pod'], float(item['value'][1])
 
-# process a time period (do prom query, process data, write output)
-# takes a KAPELConfig object and one element of output from get_time_periods
-# Remember Prometheus queries go backwards: the time instant is the end, go backwards from there.
-def process_period(config, period):
-    period_start = period['instant'] + dateutil.relativedelta.relativedelta(seconds=-period['range_sec'])
-    print(
-        f"Processing year {period['year']}, month {period['month']}, "
-        f"querying from {period['instant'].isoformat()} and going back {period['range_sec']} s to {period_start.isoformat()}."
-    )
-    queries = QueryLogic(queryRange=(str(period['range_sec']) + 's'), namespace=config.namespace)
-
-    # SSL generally not used for Prometheus access within a cluster
-    # Docs on instant query API: https://prometheus.io/docs/prometheus/latest/querying/api/#instant-queries
-    prom = PrometheusConnect(url=config.prometheus_server, disable_ssl=True)
-    prom_connect_params = {'time': period['instant'].isoformat(), 'timeout': config.query_timeout}
-
-    raw_results, results, result_lengths = {}, {}, []
-    # iterate over each query (cputime, starttime, endtime, cores) producing raw_results['cputime'] etc.
-    for query_name, query_string in vars(queries).items():
-        # Each of these raw_results is a list of dicts. Each dict in the list represents an individual data point, and contains:
-        # 'metric': a dict of one or more key-value pairs of labels, one of which is the pod name.
-        # 'value': a list in which the 0th element is the timestamp of the value, and 1th element is the actual value we're interested in.
-        print(f'Executing {query_name} query: {query_string}')
-        t1 = timer()
-        raw_results[query_name] = prom.custom_query(query=query_string, params=prom_connect_params)
-        t2 = timer()
-        results[query_name] = dict(rearrange(raw_results[query_name]))
-        result_lengths.append(len(results[query_name]))
-        t3 = timer()
-        print(f'Query finished in {t2 - t1} s, processed in {t3 - t2} s. Got {len(results[query_name])} items from {len(raw_results[query_name])} results. Peak RAM usage: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss}K.')
-        del raw_results[query_name]
 
+def record_summarized_period(config, period_start, year, month, results):
+    """ Record the sum of usage across all pods in the given time period. """
     cputime = results['cputime']
     endtime = results['endtime']
     starttime = results['starttime']
     cores = results['cores']
-
+    result_lengths = list((len(l) for l in results.values()))
     # Confirm the assumption that cputime should have the fewest entries, while starttime and cores may have additional ones
     # corresponding to jobs that have started but not finished yet, and endtime may have additional ones if there are pods without CPU resource requests.
     # We only want the jobs for which all values are available: start time, end time, CPU request.
@@ -241,16 +240,16 @@ def process_period(config, period):
 
     summary_output = summary_message(
         config,
-        year=period['year'],
-        month=period['month'],
+        year=year,
+        month=month,
         wall_time=sum_walltime,
         cpu_time=sum_cputime,
         n_jobs=len(endtime),
         # this appears faster than getting min/max during the dict iteration above
         first_end=round(min(endtime.values())),
         last_end=round(max(endtime.values()))
     )
-    sync_output = sync_message(config, year=period['year'], month=period['month'], n_jobs=len(endtime))
+    sync_output = sync_message(config, year=year, month=month, n_jobs=len(endtime))
 
     # Write output to the message queue on local filesystem
     # https://dirq.readthedocs.io/en/latest/queuesimple.html#directory-structure
@@ -262,6 +261,73 @@ def process_period(config, period):
     print(f'Writing sync record to {config.output_path}/{sync_file}:')
     print('--------------------------------\n' + sync_output + '--------------------------------')
 
+def record_individual_period(config, results):
+    """ Record each pod in the namespace over the summarized period.
+    Assumes each pod ran once and terminated upon completion.
+    """
+    # Pivot records from {'data_type':{'pod_name':value}} to {'pod_name':{'data_type':value}}
+    per_pod_records = {}
+    for data_type, records in results.items():
+        for pod, val in records.items():
+            if not pod in per_pod_records:
+                per_pod_records[pod] = {}
+            per_pod_records[pod][data_type] = val
+
+    dirq = QueueSimple(str(config.output_path))
+    for pod_name, records in per_pod_records.items():
+        # Only report on pods that have completed. Running pods won't have an endtime
+        if not ('starttime' in records and 'endtime' in records):
+            continue
+        individual_output = individual_message(
+            config, 
+            pod_name,
+            records.get('memory', 0),
+            records.get('cores', 0),
+            records['endtime'] - records['starttime'],
+            records.get('cpuusage', 0),
+            records['starttime'],
+            records['endtime'])
+        record_file = dirq.add(individual_output)
+        print(f'Writing individual record to {config.output_path}/{record_file}:')
+        print('--------------------------------\n' + individual_output + '--------------------------------')
+
+
+# process a time period (do prom query, process data, write output)
+# takes a KAPELConfig object and one element of output from get_time_periods
+# Remember Prometheus queries go backwards: the time instant is the end, go backwards from there.
+def process_period(config, period):
+    period_start = period['instant'] + dateutil.relativedelta.relativedelta(seconds=-period['range_sec'])
+    print(
+        f"Processing year {period['year']}, month {period['month']}, "
+        f"querying from {period['instant'].isoformat()} and going back {period['range_sec']} s to {period_start.isoformat()}."
+    )
+    queries = QueryLogic(queryRange=(str(period['range_sec']) + 's'), namespace=config.namespace)
+
+    # SSL generally not used for Prometheus access within a cluster
+    # Docs on instant query API: https://prometheus.io/docs/prometheus/latest/querying/api/#instant-queries
+    headers = {"Authorization": config.auth_header } if config.auth_header else None
+    prom = PrometheusConnect(url=config.prometheus_server, disable_ssl=True, headers=headers)
+    prom_connect_params = {'time': period['instant'].isoformat(), 'timeout': config.query_timeout}
+
+    results = {}
+    # iterate over each query (cputime, starttime, endtime, cores) producing raw_results['cputime'] etc.
+    for query_name, query_string in vars(queries).items():
+        # Each of these raw_results is a list of dicts. Each dict in the list represents an individual data point, and contains:
+        # 'metric': a dict of one or more key-value pairs of labels, one of which is the pod name.
+        # 'value': a list in which the 0th element is the timestamp of the value, and 1th element is the actual value we're interested in.
+        print(f'Executing {query_name} query: {query_string}')
+        t1 = timer()
+        raw_result = prom.custom_query(query=query_string, params=prom_connect_params)
+        t2 = timer()
+        results[query_name] = dict(rearrange(raw_result))
+        t3 = timer()
+        print(f'Query finished in {t2 - t1} s, processed in {t3 - t2} s. Got {len(results[query_name])} items from {len(raw_result)} results. Peak RAM usage: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss}K.')
+
+    if config.summarize_records:
+        record_summarized_period(config, period_start, period['year'], period['month'], results)
+    else:
+        record_individual_period(config, results)
+
 def main(envFile):
     print(f'Starting KAPEL processor: {__file__} with envFile {envFile} at {datetime.datetime.now(tz=datetime.timezone.utc).isoformat()}')
     cfg = KAPELConfig(envFile)

python/KAPELConfig.py

@@ -14,12 +14,17 @@ def __init__(self, envFile=None):
         # Format: validity is determined by python urllib.parse.
         self.prometheus_server = env.url("PROMETHEUS_SERVER", "http://kube-prometheus-prometheus.kube-prometheus:9090").geturl()
 
+        # Optionally add authentication headers - these are passed in under "Authorization"
+        self.auth_header = env.str("PROMETHEUS_AUTH_HEADER", None)
+
         # The default behaviour ("auto" mode) is to publish records for the previous month, and up to the current day of the current month.
         self.publishing_mode = env.str("PUBLISHING_MODE", "auto")
 
         # The Kubernetes namespace to query. Only pods in this namespace will be accounted.
         self.namespace = env.str("NAMESPACE")
 
+        self.summarize_records = env.bool("SUMMARIZE_RECORDS", True)
+
         # If PUBLISHING_MODE is "gap" instead, then a fixed time period will be queried instead and we need the start and end to be specified.
         # Format: ISO 8601, like "2020-12-20T07:20:50.52Z", to avoid complications with time zones and leap seconds.
         # Timezone should be specified, and it should be UTC for consistency with the auto mode publishing.