Cloud Monitor:Query optimization and debugging

Basic syntax

Add the DryRun setting before any UModel PaaS query:

.set "umodel_paas_mode"='dry_run';

-- Your UModel PaaS query

Phase 1 Table pattern example

.set "umodel_paas_mode"='dry_run';

.metric_set with(
    domain='apm', 
    name='apm.metric.apm.service', 
    source='metrics', 
    metric='request_count',
    query='service_id = "order-service"'
)

Response:

.metricstore with(region='cn-hangzhou', project='cms-cms-umodel-paas-0620-kffw8bjagidkeyc2', metricstore='metricstore-apm') 
| prom-call promql_query_range('sum by (acs_arms_service_id) (rate(arms_app_requests_count_raw{acs_arms_service_id="order-service"}[1m]))', '1m')

Phase 2 Object pattern example

.set "umodel_paas_mode"='dry_run';

.entity_set with(domain='apm', name='apm.service', ids=['1ab40caaf6xa707e6'])
| entity-call get_golden_metrics()

Response:

.let golden_metric_request_count_1=.metricstore with(project='proj-xtrace-xx-ap-southeast-3', metricstore='metricstore-apm-metrics') | prom-call promql_query_range('sum(sum_over_time_lcro(arms_app_requests_count_ign_destid_endpoint_parent_ppid_prpc_rpc{acs_arms_service_id="hwx28v3j7p@xx",callKind=~"http|rpc|custom_entry|server|consumer|schedule",service="payment"}[1m]))') | extend metric = 'request_count', metric_set_id = 'apm@metric_set@apm.metric.apm.service'; .let golden_metric_error_count_2=.metricstore with(project='proj-xtrace-xx-ap-southeast-3', metricstore='metricstore-apm-metrics') | prom-call promql_query_range('sum(sum_over_time_lcro(arms_app_requests_error_count_ign_destid_endpoint_parent_ppid_prpc_rpc{acs_arms_service_id="hwx28v3j7p@xx",callKind=~"http|rpc|custom_entry|server|consumer|schedule",service="payment"}[1m]))') | extend metric = 'error_count', metric_set_id = 'apm@metric_set@apm.metric.apm.service'; .let golden_metric_avg_request_latency_seconds_3=.metricstore with(project='proj-xtrace-xx-ap-southeast-3', metricstore='metricstore-apm-metrics') | prom-call promql_query_range('sum(sum_over_time_lcro(arms_app_requests_seconds_ign_destid_endpoint_parent_ppid_prpc_rpc{acs_arms_service_id="hwx28v3j7p@xx",callKind=~"http|rpc|custom_entry|server|consumer|schedule",service="payment"}[1m])) / sum(sum_over_time_lcro(arms_app_requests_count_ign_destid_endpoint_parent_ppid_prpc_rpc{acs_arms_service_id="hwx28v3j7p@xx",callKind=~"http|rpc|custom_entry|server|consumer|schedule",service="payment"}[1m]))') | extend metric = 'avg_request_latency_seconds', metric_set_id = 'apm@metric_set@apm.metric.apm.service'; $golden_metric_request_count_1 | union $golden_metric_error_count_2, $golden_metric_avg_request_latency_seconds_3

1.3.1 Field mapping validation

Use DryRun to check whether the field mappings are correct:

.set "umodel_paas_mode"='dry_run';

-- Check how entity IDs are mapped to storage fields through property information
.entity_set with(domain='apm', name='apm.service', id=['1ab40caaf6xa707e6'])
| entity-call get_metric('apm', 'apm.metric.apm.service', 'request_count')

-- Check how entity query fields are mapped to storage fields
.entity_set with(domain='apm', name='apm.service', query='service = "order-service"')
| entity-call get_metric('apm', 'apm.metric.apm.service', 'request_count')

Verify that the field mapping rules in the returned SPL are applied as expected.

1.3.2 Filter condition check

Verify the transformation of complex filter conditions:

.set "umodel_paas_mode"='dry_run';

.entity_set with(domain='apm', name='apm.service', 
    query='service_id = "order-service" and region = "cn-hangzhou"')
| entity-call get_metric('apm', 'apm.metric.apm.service', 'request_count')

Verify that the filter conditions in the generated SPL are complete and correct.

1.3.3 Relational query debugging

Verify the field mappings for entity relational queries:

.set "umodel_paas_mode"='dry_run';

.entity_set with(domain='apm', name='apm.service', query='service = "service-a"')
| entity-call get_relation_golden_metrics(
    'apm', 'apm.service', [], 'service = "service-b"',
    'calls', 'out', 'range', '5m')

Verify that the filter conditions for the source and target entities are combined correctly.

2.1.1 Filter condition optimization

Best practice: Provide precise filter conditions to reduce the amount of data scanned and improve query response speed.

• Note: If you are using the phase 2 pattern, specify the entity IDs for the query using `ids` whenever possible. This allows UModel to handle the query transformation.

.profile_set with(
    domain='apm', 
    name='apm.profile.cpu',
    query='service_id = "order-service" and profile_type = "cpu"'
)

.entity_set with(domain='apm', name='apm.service', id=['1ab40caaf6xa707e6'])
| entity-call get_metric('apm', 'apm.metric.apm.service', 'request_count')

2.1.2 Metric pattern step size settings

Set a reasonable time step size for the metric pattern:

• Note: Do not manually set the step size unless necessary. The built-in step size logic in UModel automatically selects an appropriate step size based on the query time range and data volume.

2.1.3 Aggregation policy optimization

For metric-based queries, prioritize using aggregation methods. Otherwise, the query may return a large amount of data and adversely affect the response time.

-- Recommended: Use an aggregation method. To get a specific dimension, specify the aggregation dimension.

.metric_set with(
    domain='apm', 
    name='apm.metric.apm.service', 
    source='metrics', 
    metric='request_count',
    aggregate=true, -- Do not specify a dimension. Aggregate all metrics into a single line.
)

.metric_set with(
    domain='apm', 
    name='apm.metric.apm.service', 
    source='metrics', 
    metric='request_count',
    aggregate=true,
    aggregate_labels=['region']  -- Aggregate only the necessary dimensions.
)

-- Not recommended: Do not aggregate. This returns all dimension combinations and results in a large amount of data.
.metric_set with(
    domain='apm', 
    name='apm.metric.apm.service', 
    source='metrics', 
    metric='request_count',
    aggregate=false  -- Returns all dimension combinations.
)

2.2.1 Method list cache

-- Cache the entity method list (relatively stable)
.entity_set with(domain='apm', name='apm.service')
| entity-call __list_method__()

Cache policy:

• Cache duration: 1 to 24 hours.

• Invalidation condition: UModel configuration updates.

• Scenario: Frequent method discovery calls.

2.2.2 Query result cache

Queries suitable for caching:

-- Relatively stable configuration query
.entity_set with(domain='apm', name='apm.service')
| entity-call list_data_set(['metric_set'], true)

-- Tag value query (changes infrequently)
.entity_set with(domain='apm', name='apm.service')
| entity-call get_label_values('apm', 'apm.metric.apm.service', 'region')

Cache policy:

• Cache duration: 5 to 60 minutes.

• Invalidation conditions: Data updates or configuration changes.

• Scenario: High-frequency, repetitive queries.

Step 1: Verify the basic configuration

-- 1. Check if the entity exists
.entity_set with(domain='apm', name='apm.service')
| entity-call get_entity_set()

-- 2. Check the associated dataset
.entity_set with(domain='apm', name='apm.service')
| entity-call list_data_set()

Step 2: Test a simple query

-- 3. Test a basic method call
.set "umodel_paas_mode"='dry_run';

.entity_set with(domain='apm', name='apm.service')
| entity-call __list_method__()

Step 3: Gradually increase complexity

-- 4. Add a filter condition
.set "umodel_paas_mode"='dry_run';

.entity_set with(domain='apm', name='apm.service', query='service_id = "order-service"')
| entity-call get_golden_metrics()

-- 5. Test a relational query
.entity_set with(domain='apm', name='apm.service', query='service_id = "order-service"')
| entity-call get_relation_golden_metrics('apm', 'apm.operation', [], '', 'contains', 'out')