diff --git a/core/pandajob/summary_error.py b/core/pandajob/summary_error.py index 186ff3fb..252620b1 100644 --- a/core/pandajob/summary_error.py +++ b/core/pandajob/summary_error.py @@ -162,8 +162,9 @@ def build_error_histograms(jobs, is_wn_instead_of_site=False): 'modificationtime': job['modificationtime'], 'site': job['computingsite'] if not is_wn_instead_of_site else job['wn'], 'code': ','.join(sorted(get_job_error_categories(job))), - 'task': job['jeditaskid'], + 'task': str(job['jeditaskid']), 'user': job['produsername'], + 'request': str(job['reqid']), }) timestamp_list.append(job['modificationtime']) @@ -175,12 +176,12 @@ def build_error_histograms(jobs, is_wn_instead_of_site=False): df.set_index('modificationtime', inplace=True) # Apply the function to each column where you want low-impact values grouped - for column in ['site', 'code', 'task', 'user']: + for column in ['site', 'code', 'task', 'user', 'request']: df = categorize_low_impact_by_percentage(df, column, threshold_percent) # Generate JSON-ready data for each column output_data = {} - for column in ['site', 'code', 'task', 'user']: + for column in ['site', 'code', 'task', 'user', 'request']: output_data[column] = prepare_binned_and_total_data(df, column, freq=freq) total_jobs_per_bin = df.resample(freq).size().reset_index(name='total') diff --git a/core/templates/errorSummary.html b/core/templates/errorSummary.html index f6624b6a..ce9de087 100644 --- a/core/templates/errorSummary.html +++ b/core/templates/errorSummary.html @@ -62,6 +62,7 @@
Worker node slot performance {% if wnname != 'all' %} for node {{ wnname }} {% endif %} at {{ site }}, sort by: - {% if 'sortby' in requestParams %}{% if requestParams.sortby == 'alpha' %}count{% else %}alpha{% endif %}{% else %}count{% endif %}
+ {% if 'sortby' in requestParams %}{% if requestParams.sortby == 'alpha' %}count{% else %}alpha{% endif %}{% else %}count,{% endif %} + copy time locked link + {% endif %} {% if wnPlotFailed %} @@ -258,6 +261,21 @@