Repository: risingwavelabs/risingwave (language: rust)

Issue:
per actor_id metrics in `Streaming` section causing too much data shown on the dashboard
Including the following items under `streaming` section:
```
Sink Throughput(rows/s) per Partition
Sink Throughput(MB/s) per Partition
Materialized View Throughput(rows/s) per Partition
Backfill Snapshot Read Throughput(rows)
Backfill Upstream Throughput(rows)
```


Discussion / hints:

Fix patch:
diff --git a/docker/dashboards/risingwave-dev-dashboard.json b/docker/dashboards/risingwave-dev-dashboard.json
index 59a999ebd238f..d966c319a8fda 100644
--- a/docker/dashboards/risingwave-dev-dashboard.json
+++ b/docker/dashboards/risingwave-dev-dashboard.json
@@ -1,1 +1,1 @@
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diff --git a/docker/dashboards/risingwave-user-dashboard.json b/docker/dashboards/risingwave-user-dashboard.json
index cca78d327f481..fe24fa71cde22 100644
--- a/docker/dashboards/risingwave-user-dashboard.json
+++ b/docker/dashboards/risingwave-user-dashboard.json
@@ -1,1 +1,1 @@
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instance))","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"histogram_quantile(0.99, sum(rate(frontend_latency_local_execution_bucket{job=~\"$job\",instance=~\"$node\"}[$__rate_interval])) by (le, job, instance))","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"p99 - {{job}} @ {{instance}}","metric":"","query":"histogram_quantile(0.99, sum(rate(frontend_latency_local_execution_bucket{job=~\"$job\",instance=~\"$node\"}[$__rate_interval])) by (le, job, instance))","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"histogram_quantile(1.0, sum(rate(frontend_latency_local_execution_bucket{job=~\"$job\",instance=~\"$node\"}[$__rate_interval])) by (le, job, instance))","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"pmax - {{job}} @ {{instance}}","metric":"","query":"histogram_quantile(1.0, sum(rate(frontend_latency_local_execution_bucket{job=~\"$job\",instance=~\"$node\"}[$__rate_interval])) by (le, job, instance))","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"Query Latency in Local Execution Mode","transformations":[],"transparent":false,"type":"timeseries"}],"repeat":null,"repeatDirection":null,"span":null,"targets":[],"timeFrom":null,"timeShift":null,"title":"Batch","transformations":[],"transparent":false,"type":"row"}],"refresh":"","rows":[],"schemaVersion":12,"sharedCrosshair":true,"style":"dark","tags":["risingwave"],"templating":{"list":[{"current":{"selected":false,"text":"All","value":"__all"},"definition":"label_values(process_cpu_seconds_total, instance)","description":"Reporting instance of the metric","hide":0,"includeAll":true,"label":"Node","multi":true,"name":"node","options":[],"query":{"query":"label_values(process_cpu_seconds_total, instance)","refId":"StandardVariableQuery"},"refresh":2,"regex":"","skipUrlSync":false,"sort":6,"type":"query"},{"current":{"selected":false,"text":"All","value":"__all"},"definition":"label_values(process_cpu_seconds_total, job)","description":"Reporting job of the metric","hide":0,"includeAll":true,"label":"Job","multi":true,"name":"job","options":[],"query":{"query":"label_values(process_cpu_seconds_total, job)","refId":"StandardVariableQuery"},"refresh":2,"regex":"","skipUrlSync":false,"sort":6,"type":"query"}]},"time":{"from":"now-30m","to":"now"},"timepicker":{"hidden":false,"refresh_intervals":["5s","10s","30s","1m","5m","15m","30m","1h","2h","1d"],"time_options":["5m","15m","1h","6h","12h","24h","2d","7d","30d"]},"timezone":"browser","title":"risingwave_dashboard","uid":"Fcy3uV1nz","version":0}
diff --git a/grafana/risingwave-dev-dashboard.dashboard.py b/grafana/risingwave-dev-dashboard.dashboard.py
index c3f87300b867e..489246f5fee46 100644
--- a/grafana/risingwave-dev-dashboard.dashboard.py
+++ b/grafana/risingwave-dev-dashboard.dashboard.py
@@ -951,75 +951,68 @@ def section_streaming(outer_panels):
                 # TODO: These 2 metrics should be deprecated because they are unaware of Log Store
                 # Let's remove them when all sinks are migrated to Log Store
                 panels.timeseries_rowsps(
-                    "Sink Throughput(rows/s)",
-                    "The number of rows streamed into each sink per second. For sinks with 'sink_decouple = true', please refer to the 'Sink Metrics' section",
+                    "Sink Executor Throughput (rows/s)",
+                    "The number of rows streamed into the SinkExecutor per second. For sinks with 'sink_decouple = true', please refer to the 'Sink Metrics' section",
                     [
                         panels.target(
                             f"sum(rate({metric('stream_sink_input_row_count')}[$__rate_interval])) by (sink_id) * on(sink_id) group_left(sink_name) group({metric('sink_info')}) by (sink_id, sink_name)",
                             "sink {{sink_id}} {{sink_name}}",
                         ),
-                    ],
-                ),
-                panels.timeseries_rowsps(
-                    "Sink Throughput(rows/s) per Partition",
-                    "The number of rows streamed into each sink per second. For sinks with 'sink_decouple = true', please refer to the 'Sink Metrics' section",
-                    [
-                        panels.target(
+                        panels.target_hidden(
                             f"sum(rate({metric('stream_sink_input_row_count')}[$__rate_interval])) by (sink_id, actor_id) * on(actor_id) group_left(sink_name) {metric('sink_info')}",
                             "sink {{sink_id}} {{sink_name}} - actor {{actor_id}}",
                         ),
                     ],
                 ),
                 panels.timeseries_bytesps(
-                    "Sink Throughput(MB/s)",
-                    "The figure shows the number of bytes written each sink per second. For sinks with 'sink_decouple = true', please refer to the 'Sink Metrics' section",
+                    "Sink Executor Throughput (MB/s)",
+                    "The figure shows the number of bytes written SinkExecutor per second. For sinks with 'sink_decouple = true', please refer to the 'Sink Metrics' section",
                     [
                         panels.target(
                             f"(sum(rate({metric('stream_sink_input_bytes')}[$__rate_interval])) by (sink_id) * on(sink_id) group_left(sink_name) group({metric('sink_info')}) by (sink_id, sink_name)) / (1000*1000)",
                             "sink {{sink_id}} {{sink_name}}",
                         ),
-                    ],
-                ),
-                panels.timeseries_bytesps(
-                    "Sink Throughput(MB/s) per Partition",
-                    "The number of bytes streamed into each sink per second. For sinks with 'sink_decouple = true', please refer to the 'Sink Metrics' section",
-                    [
-                        panels.target(
+                        panels.target_hidden(
                             f"(sum(rate({metric('stream_sink_input_bytes')}[$__rate_interval])) by (sink_id, actor_id) * on(actor_id) group_left(sink_name) {metric('sink_info')}) / (1000*1000)",
                             "sink {{sink_id}} {{sink_name}} - actor {{actor_id}}",
                         ),
                     ],
                 ),
                 panels.timeseries_rowsps(
-                    "Materialized View Throughput(rows/s)",
+                    "Materialized View Throughput (rows/s)",
                     "The figure shows the number of rows written into each materialized view per second.",
                     [
                         panels.target(
                             f"sum(rate({table_metric('stream_mview_input_row_count')}[$__rate_interval])) by (table_id) * on(table_id) group_left(table_name) group({metric('table_info')}) by (table_id, table_name)",
                             "mview {{table_id}} {{table_name}}",
                         ),
-                    ],
-                ),
-                panels.timeseries_rowsps(
-                    "Materialized View Throughput(rows/s) per Partition",
-                    "The figure shows the number of rows written into each materialized view per second.",
-                    [
-                        panels.target(
+                        panels.target_hidden(
                             f"rate({table_metric('stream_mview_input_row_count')}[$__rate_interval]) * on(fragment_id, table_id) group_left(table_name) {metric('table_info')}",
                             "mview {{table_id}} {{table_name}} - actor {{actor_id}} fragment_id {{fragment_id}}",
                         ),
                     ],
                 ),
                 panels.timeseries_rowsps(
-                    "Backfill Snapshot Read Throughput(rows)",
-                    "Rows/sec that we read from the backfill snapshot",
+                    "Backfill Snapshot-Read Throughput (rows/s)",
+                    "Rows/sec that we read from the backfill snapshot by materialized view",
                     [
                         panels.target(
+                            f"""
+                                sum by (table_id) (
+                                  rate({metric('stream_backfill_snapshot_read_row_count', node_filter_enabled=False, table_id_filter_enabled=True)}[$__rate_interval])
+                                )
+                                * on(table_id) group_left(table_name) (
+                                  group({metric('table_info', node_filter_enabled=False)}) by (table_name, table_id)
+                                )
+                            """,
+                            "table_name={{table_name}} table_id={{table_id}}",
+                        ),
+                        panels.target_hidden(
                             f"rate({table_metric('stream_backfill_snapshot_read_row_count')}[$__rate_interval])",
                             "table_id={{table_id}} actor={{actor_id}} @ {{%s}}"
                             % NODE_LABEL,
                         ),
-                        panels.target(
+                        panels.target_hidden(
                             f"rate({table_metric('stream_snapshot_backfill_consume_snapshot_row_count')}[$__rate_interval])",
                             "table_id={{table_id}} actor={{actor_id}} {{stage}} @ {{%s}}"
                             % NODE_LABEL,
@@ -1027,27 +1020,21 @@ def section_streaming(outer_panels):
                     ],
                 ),
                 panels.timeseries_rowsps(
-                    "Backfill Snapshot Read Throughput(rows) by MV",
-                    "Rows/sec that we read from the backfill snapshot by materialized view",
+                    "Backfill Upstream Throughput (rows/s)",
+                    "Total number of rows that have been output from the backfill upstream",
                     [
                         panels.target(
                             f"""
                                 sum by (table_id) (
-                                  rate({metric('stream_backfill_snapshot_read_row_count', node_filter_enabled=False, table_id_filter_enabled=True)}[$__rate_interval])
+                                  rate({metric('stream_backfill_upstream_output_row_count', node_filter_enabled=False, table_id_filter_enabled=True)}[$__rate_interval])
                                 )
                                 * on(table_id) group_left(table_name) (
                                   group({metric('table_info', node_filter_enabled=False)}) by (table_name, table_id)
                                 )
                             """,
                             "table_name={{table_name}} table_id={{table_id}}",
-                        )
-                    ],
-                ),
-                panels.timeseries_rowsps(
-                    "Backfill Upstream Throughput(rows)",
-                    "Total number of rows that have been output from the backfill upstream",
-                    [
-                        panels.target(
+                        ),
+                        panels.target_hidden(
                             f"rate({table_metric('stream_backfill_upstream_output_row_count')}[$__rate_interval])",
                             "table_id={{table_id}} actor={{actor_id}} @ {{%s}}"
                             % NODE_LABEL,
@@ -1328,7 +1315,7 @@ def section_streaming_actors(outer_panels: Panels):
                             f"sum(rate({metric('stream_actor_out_record_cnt')}[$__rate_interval])) by (fragment_id)",
                             "fragment total {{fragment_id}}",
                         ),
-                        panels.target(
+                        panels.target_hidden(
                             f"rate({metric('stream_actor_out_record_cnt', actor_level_filter)}[$__rate_interval])",
                             "actor {{actor_id}}",
                         ),
@@ -1342,7 +1329,7 @@ def section_streaming_actors(outer_panels: Panels):
                             f"sum({metric('stream_memory_usage')}) by (table_id, desc)",
                             "table total {{table_id}}: {{desc}}",
                         ),
-                        panels.target(
+                        panels.target_hidden(
                             f"{metric('stream_memory_usage', actor_level_filter)}",
                             "actor {{actor_id}} table {{table_id}}: {{desc}}",
                         ),
@@ -1384,11 +1371,11 @@ def section_streaming_actors(outer_panels: Panels):
                             f"sum(rate({table_metric('stream_materialize_cache_total_count')}[$__rate_interval])) by (table_id, fragment_id)",
                             "total cached count - table {{table_id}} fragment {{fragment_id}}",
                         ),
-                        panels.target(
+                        panels.target_hidden(
                             f"rate({table_metric('stream_materialize_cache_hit_count', actor_level_filter)}[$__rate_interval])",
                             "cache hit count - actor {{actor_id}} table {{table_id}} fragment {{fragment_id}}",
                         ),
-                        panels.target(
+                        panels.target_hidden(
                             f"rate({table_metric('stream_materialize_cache_total_count', actor_level_filter)}[$__rate_interval])",
                             "total cached count - actor {{actor_id}} table {{table_id}} fragment {{fragment_id}}",
                         ),
@@ -1484,8 +1471,7 @@ def section_streaming_actors(outer_panels: Panels):
                         ),
                         panels.target(
                             f"1 - (sum(rate({metric('stream_materialize_cache_hit_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_materialize_cache_total_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
-                            "Materialize executor cache miss ratio - table {{table_id}} fragment {{fragment_id}}  {{%s}}"
-                            % NODE_LABEL,
+                            "Materialize executor cache miss ratio - table {{table_id}} fragment {{fragment_id}}"
                         ),
                         panels.target(
                             f"(sum(rate({metric('stream_over_window_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_over_window_cache_lookup_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
@@ -1512,7 +1498,7 @@ def section_streaming_actors(outer_panels: Panels):
                             / ignoring (wait_side, executor) group_left sum({metric('stream_actor_count')}) by (fragment_id)",
                             "fragment avg {{fragment_id}} {{wait_side}} {{executor}}",
                         ),
-                        panels.target(
+                        panels.target_hidden(
                             f"rate({metric('stream_barrier_align_duration_ns', actor_level_filter)}[$__rate_interval]) / 1000000000",
                             "actor {{actor_id}} fragment {{fragment_id}} {{wait_side}} {{executor}}",
                         ),
@@ -1744,7 +1730,7 @@ def section_streaming_actors(outer_panels: Panels):
                             f"sum(rate({metric('stream_executor_row_count')}[$__rate_interval])) by (executor_identity, fragment_id)",
                             "{{executor_identity}} fragment total {{fragment_id}}",
                         ),
-                        panels.target(
+                        panels.target_hidden(
                             f"rate({metric('stream_executor_row_count', actor_level_filter)}[$__rate_interval])",
                             "{{executor_identity}} actor {{actor_id}}",
                         ),
@@ -4183,8 +4169,8 @@ def section_sink_metrics(outer_panels):
                     "The rows sent by remote sink to the Java connector process",
                     [
                         panels.target(
-                            f"rate({metric('connector_sink_rows_received')}[$__rate_interval])",
-                            "{{sink_id}} {{sink_name}} @ actor {{actor_id}}",
+                            f"sum(rate({metric('connector_sink_rows_received')}[$__rate_interval])) by (sink_id, sink_name)",
+                            "{{sink_id}} {{sink_name}}",
                         ),
                     ],
                 ),
diff --git a/grafana/risingwave-dev-dashboard.json b/grafana/risingwave-dev-dashboard.json
index 59a999ebd238f..d966c319a8fda 100644
--- a/grafana/risingwave-dev-dashboard.json
+++ b/grafana/risingwave-dev-dashboard.json
@@ -1,1 +1,1 @@
-{"__inputs":[],"annotations":{"list":[]},"description":"RisingWave Dev Dashboard","editable":true,"gnetId":null,"graphTooltip":0,"hideControls":false,"id":null,"links":[],"panels":[{"cacheTimeout":null,"collapsed":true,"datasource":null,"description":null,"editable":true,"error":false,"fieldConfig":{"defaults":{"thresholds":{"mode":"absolute","steps":[]}}},"gridPos":{"h":1,"w":24,"x":0,"y":0},"height":null,"hideTimeOverride":false,"id":1,"interval":null,"links":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"panels":[{"cacheTimeout":null,"color":{"mode":"thresholds"},"columns":[],"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"description":"Information about actors","editable":true,"error":false,"fieldConfig":{"defaults":{"custom":{"align":"auto","displayMode":"auto","filterable":true},"thresholds":{"mode":"absolute","steps":[]}},"overrides":[]},"fontSize":"100%","gridPos":{"h":8,"w":12,"x":0,"y":0},"height":null,"hideTimeOverride":false,"id":2,"interval":null,"links":[],"mappings":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"options":{"showHeader":true},"repeat":null,"repeatDirection":null,"span":6,"targets":[{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"group(actor_info{job=~\"$job\",instance=~\"$node\"}) by (actor_id, fragment_id, compute_node)","format":"table","hide":false,"instant":true,"interval":"","intervalFactor":2,"legendFormat":"","metric":"","query":"group(actor_info{job=~\"$job\",instance=~\"$node\"}) by (actor_id, fragment_id, compute_node)","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"Actor Info","transformations":[{"id":"organize","options":{"excludeByName":{"Time":true,"Value":true},"indexByName":{"actor_id":0,"compute_node":2,"fragment_id":1}}}],"transparent":false,"type":"table"},{"cacheTimeout":null,"color":{"mode":"thresholds"},"columns":[],"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"description":"Information about state tables. Column `materialized_view_id` is the id of the materialized view that this state table belongs to.","editable":true,"error":false,"fieldConfig":{"defaults":{"custom":{"align":"auto","displayMode":"auto","filterable":true},"thresholds":{"mode":"absolute","steps":[]}},"overrides":[]},"fontSize":"100%","gridPos":{"h":8,"w":12,"x":12,"y":0},"height":null,"hideTimeOverride":false,"id":3,"interval":null,"links":[],"mappings":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"options":{"showHeader":true},"repeat":null,"repeatDirection":null,"span":6,"targets":[{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"group(table_info{job=~\"$job\",instance=~\"$node\"}) by (table_id, table_name, table_type, materialized_view_id, fragment_id, compaction_group_id)","format":"table","hide":false,"instant":true,"interval":"","intervalFactor":2,"legendFormat":"","metric":"","query":"group(table_info{job=~\"$job\",instance=~\"$node\"}) by (table_id, table_name, table_type, materialized_view_id, fragment_id, compaction_group_id)","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"State Table Info","transformations":[{"id":"organize","options":{"excludeByName":{"Time":true,"Value":true},"indexByName":{"compaction_group_id":5,"fragment_id":4,"materialized_view_id":3,"table_id":0,"table_name":1,"table_type":2}}}],"transparent":false,"type":"table"},{"cacheTimeout":null,"color":{"mode":"thresholds"},"columns":[],"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"description":"Actor count per compute node","editable":true,"error":false,"fieldConfig":{"defaults":{"custom":{"align":"auto","displayMode":"auto","filterable":true},"thresholds":{"mode":"absolute","steps":[]}},"overrides":[]},"fontSize":"100%","gridPos":{"h":8,"w":12,"x":0,"y":8},"height":null,"hideTimeOverride":false,"id":4,"interval":null,"links":[],"mappings":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"options":{"showHeader":true},"repeat":null,"repeatDirection":null,"span":6,"targets":[{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"count(actor_info{job=~\"$job\",instance=~\"$node\"}) by (compute_node)","format":"table","hide":false,"instant":true,"interval":"","intervalFactor":2,"legendFormat":"","metric":"","query":"count(actor_info{job=~\"$job\",instance=~\"$node\"}) by (compute_node)","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"Actor Count (Group By Compute Node)","transformations":[{"id":"organize","options":{"excludeByName":{"Time":true},"indexByName":{"compaction_group_id":5,"fragment_id":4,"materialized_view_id":3,"table_id":0,"table_name":1,"table_type":2}}}],"transparent":false,"type":"table"}],"repeat":null,"repeatDirection":null,"span":null,"targets":[],"timeFrom":null,"timeShift":null,"title":"Actor/Table Id Info","transformations":[],"transparent":false,"type":"row"},{"cacheTimeout":null,"collapsed":true,"datasource":null,"description":null,"editable":true,"error":false,"fieldConfig":{"defaults":{"thresholds":{"mode":"absolute","steps":[]}}},"gridPos":{"h":1,"w":24,"x":0,"y":1},"height":null,"hideTimeOverride":false,"id":5,"interval":null,"links":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"panels":[{"cacheTimeout":null,"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"description":"The number of each type of RisingWave components alive.","editable":true,"error":false,"fieldConfig":{"defaults":{"color":{"mode":"palette-classic"},"custom":{"axisLabel":"","axisPlacement":"auto","barAlignment":0,"drawStyle":"line","fillOpacity":10,"gradientMode":"none","hideFrom":{"legend":false,"tooltip":false,"viz":false},"lineInterpolation":"linear","lineWidth":1,"pointSize":5,"scaleDistribution":{"log":2,"type":"linear"},"showPoints":"auto","spanNulls":false,"stacking":{},"thresholdsStyle":{"mode":"off"}},"mappings":[],"thresholds":{"mode":"absolute","steps":[]},"unit":""},"overrides":[]},"gridPos":{"h":8,"w":12,"x":0,"y":0},"height":null,"hideTimeOverride":false,"id":6,"interval":"1s","links":[],"maxDataPoints":1000,"maxPerRow":null,"minSpan":null,"options":{"legend":{"calcs":["last"],"displayMode":"table","placement":"bottom"},"tooltip":{"mode":"single"}},"repeat":null,"repeatDirection":null,"span":null,"targets":[{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"sum(worker_num{job=~\"$job\",instance=~\"$node\"}) by (worker_type)","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"{{worker_type}}","metric":"","query":"sum(worker_num{job=~\"$job\",instance=~\"$node\"}) by (worker_type)","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"Node Count","transformations":[],"transparent":false,"type":"timeseries"},{"cacheTimeout":null,"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"description":"The memory usage of each RisingWave 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52428800","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"Lagging Compaction","metric":"","query":"sum(label_replace(storage_level_total_file_size{job=~\"$job\",instance=~\"$node\"}, 'L0', 'L0', 'level_index', '.*_L0') unless storage_level_total_file_size{job=~\"$job\",instance=~\"$node\"}) by (L0) >= bool 52428800","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"storage_stale_object_count{job=~\"$job\",instance=~\"$node\"} >= bool 200","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"Lagging Vacuum","metric":"","query":"storage_stale_object_count{job=~\"$job\",instance=~\"$node\"} >= bool 200","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"state_store_meta_cache_usage_ratio{job=~\"$job\",instance=~\"$node\"} >= bool 1.1","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"Abnormal Meta Cache Memory","metric":"","query":"state_store_meta_cache_usage_ratio{job=~\"$job\",instance=~\"$node\"} >= bool 1.1","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"state_store_block_cache_usage_ratio{job=~\"$job\",instance=~\"$node\"} >= bool 1.1","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"Abnormal Block Cache Memory","metric":"","query":"state_store_block_cache_usage_ratio{job=~\"$job\",instance=~\"$node\"} >= bool 1.1","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"state_store_uploading_memory_usage_ratio{job=~\"$job\",instance=~\"$node\"} >= bool 0.7","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"Abnormal Uploading Memory Usage","metric":"","query":"state_store_uploading_memory_usage_ratio{job=~\"$job\",instance=~\"$node\"} >= bool 0.7","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"storage_write_stop_compaction_groups{job=~\"$job\",instance=~\"$node\"} > bool 0","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"Write Stall","metric":"","query":"storage_write_stop_compaction_groups{job=~\"$job\",instance=~\"$node\"} > bool 0","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"storage_version_size{job=~\"$job\",instance=~\"$node\"} >= bool 314572800","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"Abnormal Version Size","metric":"","query":"storage_version_size{job=~\"$job\",instance=~\"$node\"} >= bool 314572800","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"storage_delta_log_count{job=~\"$job\",instance=~\"$node\"} >= bool 5000","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"Abnormal Delta Log Number","metric":"","query":"storage_delta_log_count{job=~\"$job\",instance=~\"$node\"} >= bool 5000","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"state_store_event_handler_pending_event{job=~\"$job\",instance=~\"$node\"} >= bool 10000000","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"Abnormal Pending Event Number","metric":"","query":"state_store_event_handler_pending_event{job=~\"$job\",instance=~\"$node\"} >= bool 10000000","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"object_store_failure_count{job=~\"$job\",instance=~\"$node\"} >= bool 50","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"Abnormal Object Storage Failure","metric":"","query":"object_store_failure_count{job=~\"$job\",instance=~\"$node\"} >= bool 50","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"Alerts","transformations":[],"transparent":false,"type":"timeseries"},{"cacheTimeout":null,"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"description":"Errors in the system group by type","editable":true,"error":false,"fieldConfig":{"defaults":{"color":{"mode":"palette-classic"},"custom":{"axisLabel":"","axisPlacement":"auto","barAlignment":0,"drawStyle":"line","fillOpacity":10,"gradientMode":"none","hideFrom":{"legend":false,"tooltip":false,"viz":false},"lineInterpolation":"linear","lineWidth":1,"pointSize":5,"scaleDistribution":{"log":2,"type":"linear"},"showPoints":"auto","spanNulls":false,"stacking":{},"thresholdsStyle":{"mode":"off"}},"mappings":[],"thresholds":{"mode":"absolute","steps":[]},"unit":""},"overrides":[]},"gridPos":{"h":8,"w":12,"x":12,"y":28},"height":null,"hideTimeOverride":false,"id":338,"interval":"1s","links":[],"maxDataPoints":1000,"maxPerRow":null,"minSpan":null,"options":{"legend":{"calcs":["mean"],"displayMode":"table","placement":"bottom"},"tooltip":{"mode":"single"}},"repeat":null,"repeatDirection":null,"span":null,"targets":[{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"sum(user_compute_error{job=~\"$job\",instance=~\"$node\"}) by (error_type, executor_name, fragment_id)","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"{{error_type}} @ {{executor_name}} (fragment_id={{fragment_id}})","metric":"","query":"sum(user_compute_error{job=~\"$job\",instance=~\"$node\"}) by (error_type, executor_name, fragment_id)","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"sum(user_source_error{job=~\"$job\",instance=~\"$node\"}) by (error_type, source_id, source_name, fragment_id)","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"{{error_type}} @ {{source_name}} (source_id={{source_id}} fragment_id={{fragment_id}})","metric":"","query":"sum(user_source_error{job=~\"$job\",instance=~\"$node\"}) by (error_type, source_id, source_name, fragment_id)","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"sum(user_sink_error{job=~\"$job\",instance=~\"$node\"}) by (error_type, sink_id, sink_name, fragment_id)","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"{{error_type}} @ {{sink_name}} (sink_id={{sink_id}} fragment_id={{fragment_id}})","metric":"","query":"sum(user_sink_error{job=~\"$job\",instance=~\"$node\"}) by (error_type, sink_id, sink_name, fragment_id)","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"source_status_is_up{job=~\"$job\",instance=~\"$node\"} == 0","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"source error: source_id={{source_id}}, source_name={{source_name}} @ {{instance}}","metric":"","query":"source_status_is_up{job=~\"$job\",instance=~\"$node\"} == 0","refId":"","step":10,"target":""},{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"sum(rate(object_store_failure_count{job=~\"$job\",instance=~\"$node\"}[$__rate_interval])) by (instance, job, type)","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"remote storage error {{type}}: {{job}} @ {{instance}}","metric":"","query":"sum(rate(object_store_failure_count{job=~\"$job\",instance=~\"$node\"}[$__rate_interval])) by (instance, job, type)","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"Errors","transformations":[],"transparent":false,"type":"timeseries"}],"refresh":"","rows":[],"schemaVersion":12,"sharedCrosshair":true,"style":"dark","tags":["risingwave"],"templating":{"list":[{"current":{"selected":false,"text":"All","value":"__all"},"definition":"label_values(process_cpu_seconds_total, instance)","description":"Reporting instance of the metric","hide":0,"includeAll":true,"label":"Node","multi":true,"name":"node","options":[],"query":{"query":"label_values(process_cpu_seconds_total, instance)","refId":"StandardVariableQuery"},"refresh":2,"regex":"","skipUrlSync":false,"sort":6,"type":"query"},{"current":{"selected":false,"text":"All","value":"__all"},"definition":"label_values(process_cpu_seconds_total, job)","description":"Reporting job of the metric","hide":0,"includeAll":true,"label":"Job","multi":true,"name":"job","options":[],"query":{"query":"label_values(process_cpu_seconds_total, job)","refId":"StandardVariableQuery"},"refresh":2,"regex":"","skipUrlSync":false,"sort":6,"type":"query"},{"current":{"selected":false,"text":"All","value":"__all"},"definition":"label_values(table_info, table_id)","description":"Reporting table id of the metric","hide":0,"includeAll":true,"label":"Table","multi":true,"name":"table","options":[],"query":{"query":"label_values(table_info, table_id)","refId":"StandardVariableQuery"},"refresh":2,"regex":"","skipUrlSync":false,"sort":6,"type":"query"}]},"time":{"from":"now-30m","to":"now"},"timepicker":{"hidden":false,"refresh_intervals":["5s","10s","30s","1m","5m","15m","30m","1h","2h","1d"],"time_options":["5m","15m","1h","6h","12h","24h","2d","7d","30d"]},"timezone":"browser","title":"risingwave_dev_dashboard","uid":"Ecy3uV1nz","version":0}
diff --git a/grafana/risingwave-user-dashboard.dashboard.py b/grafana/risingwave-user-dashboard.dashboard.py
index 05a9e84e54c3c..c729d5842c359 100644
--- a/grafana/risingwave-user-dashboard.dashboard.py
+++ b/grafana/risingwave-user-dashboard.dashboard.py
@@ -329,10 +329,6 @@ def section_memory(outer_panels):
                     "Memory Usage (Detailed)",
                     "",
                     [
-                        panels.target(
-                            "rate(actor_memory_usage[$__rate_interval])",
-                            "streaming actor - {{actor_id}}",
-                        ),
                         panels.target(
                             f"sum({metric('state_store_meta_cache_size')}) by ({COMPONENT_LABEL},{NODE_LABEL})",
                             "storage meta cache - {{%s}} @ {{%s}}"
@@ -354,114 +350,57 @@ def section_memory(outer_panels):
                         ),
                     ],
                 ),
-                panels.timeseries_actor_ops(
-                    "Executor Cache",
-                    "Executor cache statistics",
+                panels.timeseries_percentage(
+                    "Executor Cache Miss Ratio",
+                    "",
                     [
                         panels.target(
-                            f"rate({metric('stream_join_lookup_miss_count')}[$__rate_interval])",
-                            "Join - cache miss - {{side}} side, join_table_id {{join_table_id}} degree_table_id {{degree_table_id}} actor {{actor_id}}",
-                        ),
-                        panels.target(
-                            f"rate({metric('stream_join_lookup_total_count')}[$__rate_interval])",
-                            "Join - total lookups - {{side}} side, join_table_id {{join_table_id}} degree_table_id {{degree_table_id}} actor {{actor_id}}",
-                        ),
-                        panels.target(
-                            f"rate({metric('stream_agg_lookup_miss_count')}[$__rate_interval])",
-                            "Agg - cache miss - table {{table_id}} actor {{actor_id}}",
-                        ),
-                        panels.target(
-                            f"rate({metric('stream_agg_lookup_total_count')}[$__rate_interval])",
-                            "Agg - total lookups - table {{table_id}} actor {{actor_id}}",
-                        ),
-                        panels.target(
-                            f"rate({metric('stream_agg_distinct_cache_miss_count')}[$__rate_interval])",
-                            "Distinct agg - cache miss - table {{table_id}} actor {{actor_id}}",
-                        ),
-                        panels.target(
-                            f"rate({metric('stream_agg_distinct_total_cache_count')}[$__rate_interval])",
-                            "Distinct agg - total lookups - table {{table_id}} actor {{actor_id}}",
-                        ),
-                        panels.target(
-                            f"rate({metric('stream_group_top_n_cache_miss_count')}[$__rate_interval])",
-                            "Group top n - cache miss - table {{table_id}} actor {{actor_id}}",
-                        ),
-                        panels.target(
-                            f"rate({metric('stream_group_top_n_total_query_cache_count')}[$__rate_interval])",
-                            "Group top n - total lookups - table {{table_id}} actor {{actor_id}}",
+                            f"(sum(rate({metric('stream_join_lookup_miss_count')}[$__rate_interval])) by (side, join_table_id, degree_table_id, fragment_id) ) / (sum(rate({metric('stream_join_lookup_total_count')}[$__rate_interval])) by (side, join_table_id, degree_table_id, fragment_id)) >= 0",
+                            "Join executor cache miss ratio - - {{side}} side, join_table_id {{join_table_id}} degree_table_id {{degree_table_id}} fragment {{fragment_id}}",
                         ),
                         panels.target(
-                            f"rate({metric('stream_group_top_n_appendonly_cache_miss_count')}[$__rate_interval])",
-                            "Group top n appendonly - cache miss - table {{table_id}} actor {{actor_id}}",
+                            f"(sum(rate({metric('stream_agg_lookup_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_agg_lookup_total_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
+                            "Agg cache miss ratio - table {{table_id}} fragment {{fragment_id}} ",
                         ),
                         panels.target(
-                            f"rate({metric('stream_group_top_n_appendonly_total_query_cache_count')}[$__rate_interval])",
-                            "Group top n appendonly - total lookups - table {{table_id}} actor {{actor_id}}",
+                            f"(sum(rate({metric('stream_agg_state_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_agg_state_cache_lookup_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
+                            "Agg state cache miss ratio - table {{table_id}} fragment {{fragment_id}} ",
                         ),
                         panels.target(
-                            f"rate({metric('stream_lookup_cache_miss_count')}[$__rate_interval])",
-                            "Lookup executor - cache miss - table {{table_id}} actor {{actor_id}}",
+                            f"(sum(rate({metric('stream_agg_distinct_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_agg_distinct_total_cache_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
+                            "Distinct agg cache miss ratio - table {{table_id}} fragment {{fragment_id}} ",
                         ),
                         panels.target(
-                            f"rate({metric('stream_lookup_total_query_cache_count')}[$__rate_interval])",
-                            "Lookup executor - total lookups - table {{table_id}} actor {{actor_id}}",
+                            f"(sum(rate({metric('stream_group_top_n_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_group_top_n_total_query_cache_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
+                            "Stream group top n cache miss ratio - table {{table_id}} fragment {{fragment_id}} ",
                         ),
                         panels.target(
-                            f"rate({metric('stream_temporal_join_cache_miss_count')}[$__rate_interval])",
-                            "Temporal join - cache miss - table_id {{table_id}} actor {{actor_id}}",
+                            f"(sum(rate({metric('stream_group_top_n_appendonly_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_group_top_n_appendonly_total_query_cache_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
+                            "Stream group top n appendonly cache miss ratio - table {{table_id}} fragment {{fragment_id}} ",
                         ),
                         panels.target(
-                            f"rate({metric('stream_temporal_join_total_query_cache_count')}[$__rate_interval])",
-                            "Temporal join - total lookups - table_id {{table_id}} actor {{actor_id}}",
+                            f"(sum(rate({metric('stream_lookup_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_lookup_total_query_cache_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
+                            "Stream lookup cache miss ratio - table {{table_id}} fragment {{fragment_id}} ",
                         ),
                         panels.target(
-                            f"rate({metric('stream_materialize_cache_hit_count')}[$__rate_interval])",
-                            "Materialize - cache hit count - table {{table_id}} - actor {{actor_id}}  {{%s}}"
-                            % NODE_LABEL,
+                            f"(sum(rate({metric('stream_temporal_join_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_temporal_join_total_query_cache_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
+                            "Stream temporal join cache miss ratio - table {{table_id}} fragment {{fragment_id}} ",
                         ),
                         panels.target(
-                            f"rate({metric('stream_materialize_cache_total_count')}[$__rate_interval])",
-                            "Materialize - total cache count - table {{table_id}} - actor {{actor_id}}  {{%s}}"
-                            % NODE_LABEL,
-                        ),
-                    ],
-                ),
-                panels.timeseries_percentage(
-                    "Executor Cache Miss Ratio",
-                    "",
-                    [
-                        panels.target(
-                            f"(sum(rate({metric('stream_join_lookup_miss_count')}[$__rate_interval])) by (side, join_table_id, degree_table_id, actor_id) ) / (sum(rate({metric('stream_join_lookup_total_count')}[$__rate_interval])) by (side, join_table_id, degree_table_id, actor_id)) >=0 ",
-                            "join executor cache miss ratio - - {{side}} side, join_table_id {{join_table_id}} degree_table_id {{degree_table_id}} actor {{actor_id}}",
-                        ),
-                        panels.target(
-                            f"(sum(rate({metric('stream_agg_lookup_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_agg_lookup_total_count')}[$__rate_interval])) by (table_id, actor_id)) >=0 ",
-                            "Agg cache miss ratio - table {{table_id}} actor {{actor_id}} ",
+                            f"1 - (sum(rate({metric('stream_materialize_cache_hit_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_materialize_cache_total_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
+                            "Materialize executor cache miss ratio - table {{table_id}} fragment {{fragment_id}}"
                         ),
                         panels.target(
-                            f"(sum(rate({metric('stream_agg_distinct_cache_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_agg_distinct_total_cache_count')}[$__rate_interval])) by (table_id, actor_id)) >=0",
-                            "Distinct agg cache miss ratio - table {{table_id}} actor {{actor_id}} ",
+                            f"(sum(rate({metric('stream_over_window_cache_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_over_window_cache_lookup_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
+                            "Over window cache miss ratio - table {{table_id}} fragment {{fragment_id}} ",
                         ),
                         panels.target(
-                            f"(sum(rate({metric('stream_group_top_n_cache_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_group_top_n_total_query_cache_count')}[$__rate_interval])) by (table_id, actor_id)) >=0",
-                            "Stream group top n cache miss ratio - table {{table_id}} actor {{actor_id}} ",
+                            f"(sum(rate({metric('stream_over_window_range_cache_left_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_over_window_range_cache_lookup_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
+                            "Over window partition range cache left miss ratio - table {{table_id}} fragment {{fragment_id}} ",
                         ),
                         panels.target(
-                            f"(sum(rate({metric('stream_group_top_n_appendonly_cache_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_group_top_n_appendonly_total_query_cache_count')}[$__rate_interval])) by (table_id, actor_id)) >=0",
-                            "Stream group top n appendonly cache miss ratio - table {{table_id}} actor {{actor_id}} ",
-                        ),
-                        panels.target(
-                            f"(sum(rate({metric('stream_lookup_cache_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_lookup_total_query_cache_count')}[$__rate_interval])) by (table_id, actor_id)) >=0",
-                            "Stream lookup cache miss ratio - table {{table_id}} actor {{actor_id}} ",
-                        ),
-                        panels.target(
-                            f"(sum(rate({metric('stream_temporal_join_cache_miss_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_temporal_join_total_query_cache_count')}[$__rate_interval])) by (table_id, actor_id)) >=0",
-                            "Stream temporal join cache miss ratio - table {{table_id}} actor {{actor_id}} ",
-                        ),
-                        panels.target(
-                            f"1 - (sum(rate({metric('stream_materialize_cache_hit_count')}[$__rate_interval])) by (table_id, actor_id) ) / (sum(rate({metric('stream_materialize_cache_total_count')}[$__rate_interval])) by (table_id, actor_id)) >=0",
-                            "materialize executor cache miss ratio - table {{table_id}} - actor {{actor_id}}  {{%s}}"
-                            % NODE_LABEL,
+                            f"(sum(rate({metric('stream_over_window_range_cache_right_miss_count')}[$__rate_interval])) by (table_id, fragment_id) ) / (sum(rate({metric('stream_over_window_range_cache_lookup_count')}[$__rate_interval])) by (table_id, fragment_id)) >= 0",
+                            "Over window partition range cache right miss ratio - table {{table_id}} fragment {{fragment_id}} ",
                         ),
                     ],
                 ),
@@ -691,18 +630,17 @@ def section_streaming(outer_panels):
             "Streaming",
             [
                 panels.timeseries_rowsps(
-                    "Source Throughput(rows/s)",
+                    "Source Throughput (rows/s)",
                     "The figure shows the number of rows read by each source per second.",
                     [
                         panels.target(
-                            f"rate({metric('stream_source_output_rows_counts')}[$__rate_interval])",
-                            "source={{source_name}} actor={{actor_id}} @ {{%s}}"
-                            % NODE_LABEL,
+                            f"sum(rate({metric('stream_source_output_rows_counts')}[$__rate_interval])) by (source_id, source_name, fragment_id)",
+                            "{{source_id}} {{source_name}} (fragment {{fragment_id}})",
                         ),
                     ],
                 ),
                 panels.timeseries_bytesps(
-                    "Source Throughput(MB/s)",
+                    "Source Throughput (MB/s)",
                     "The figure shows the number of bytes read by each source per second.",
                     [
                         panels.target(
@@ -712,7 +650,7 @@ def section_streaming(outer_panels):
                     ],
                 ),
                 panels.timeseries_rowsps(
-                    "Source Backfill Throughput(rows/s)",
+                    "Source Backfill Throughput (rows/s)",
                     "The figure shows the number of rows read by each source per second.",
                     [
                         panels.target(
@@ -722,7 +660,7 @@ def section_streaming(outer_panels):
                     ],
                 ),
                 panels.timeseries_rowsps(
-                    "Materialized View Throughput(rows/s)",
+                    "Materialized View Throughput (rows/s)",
                     "The figure shows the number of rows written into each materialized executor actor per second.",
                     [
                         panels.target(
@@ -732,18 +670,30 @@ def section_streaming(outer_panels):
                     ],
                 ),
                 panels.timeseries_rowsps(
-                    "Backfill Throughput(rows)",
+                    "Backfill Throughput (rows/s)",
                     "Total number of rows that have been read from the backfill operator used by MV on MV",
                     [
                         panels.target(
-                            f"rate({metric('stream_backfill_snapshot_read_row_count')}[$__rate_interval])",
-                            "Read Snapshot - table_id={{table_id}} actor={{actor_id}} @ {{%s}}"
-                            % NODE_LABEL,
-                        ),
-                        panels.target(
-                            f"rate({metric('stream_backfill_upstream_output_row_count')}[$__rate_interval])",
-                            "Upstream - table_id={{table_id}} actor={{actor_id}} @ {{%s}}"
-                            % NODE_LABEL,
+                            f"""
+                                sum by (table_id) (
+                                  rate({metric('stream_backfill_snapshot_read_row_count', node_filter_enabled=False, table_id_filter_enabled=True)}[$__rate_interval])
+                                )
+                                * on(table_id) group_left(table_name) (
+                                  group({metric('table_info', node_filter_enabled=False)}) by (table_name, table_id)
+                                )
+                            """,
+                            "{{table_id}}: {{table_name}} snapshot-read",
+                        ),
+                        panels.target(
+                            f"""
+                                sum by (table_id) (
+                                  rate({metric('stream_backfill_upstream_output_row_count', node_filter_enabled=False, table_id_filter_enabled=True)}[$__rate_interval])
+                                )
+                                * on(table_id) group_left(table_name) (
+                                  group({metric('table_info', node_filter_enabled=False)}) by (table_name, table_id)
+                                )
+                            """,
+                            "{{table_id}}: {{table_name}} upstream",
                         ),
                     ],
                 ),
diff --git a/grafana/risingwave-user-dashboard.json b/grafana/risingwave-user-dashboard.json
index cca78d327f481..fe24fa71cde22 100644
--- a/grafana/risingwave-user-dashboard.json
+++ b/grafana/risingwave-user-dashboard.json
@@ -1,1 +1,1 @@
-{"__inputs":[],"annotations":{"list":[]},"description":"RisingWave Dashboard","editable":true,"gnetId":null,"graphTooltip":0,"hideControls":false,"id":null,"links":[],"panels":[{"cacheTimeout":null,"collapsed":true,"datasource":null,"description":null,"editable":true,"error":false,"fieldConfig":{"defaults":{"thresholds":{"mode":"absolute","steps":[]}}},"gridPos":{"h":1,"w":24,"x":0,"y":0},"height":null,"hideTimeOverride":false,"id":1,"interval":null,"links":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"panels":[{"cacheTimeout":null,"color":{"mode":"thresholds"},"columns":[],"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"description":"Information about actors","editable":true,"error":false,"fieldConfig":{"defaults":{"custom":{"align":"auto","displayMode":"auto","filterable":true},"thresholds":{"mode":"absolute","steps":[]}},"overrides":[]},"fontSize":"100%","gridPos":{"h":8,"w":12,"x":0,"y":0},"height":null,"hideTimeOverride":false,"id":2,"interval":null,"links":[],"mappings":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"options":{"showHeader":true},"repeat":null,"repeatDirection":null,"span":6,"targets":[{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"group(actor_info{job=~\"$job\",instance=~\"$node\"}) by (actor_id, fragment_id, compute_node)","format":"table","hide":false,"instant":true,"interval":"","intervalFactor":2,"legendFormat":"","metric":"","query":"group(actor_info{job=~\"$job\",instance=~\"$node\"}) by (actor_id, fragment_id, compute_node)","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"Actor Info","transformations":[{"id":"organize","options":{"excludeByName":{"Time":true,"Value":true},"indexByName":{"actor_id":0,"compute_node":2,"fragment_id":1}}}],"transparent":false,"type":"table"},{"cacheTimeout":null,"color":{"mode":"thresholds"},"columns":[],"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"description":"Information about state tables. Column `materialized_view_id` is the id of the materialized view that this state table belongs to.","editable":true,"error":false,"fieldConfig":{"defaults":{"custom":{"align":"auto","displayMode":"auto","filterable":true},"thresholds":{"mode":"absolute","steps":[]}},"overrides":[]},"fontSize":"100%","gridPos":{"h":8,"w":12,"x":12,"y":0},"height":null,"hideTimeOverride":false,"id":3,"interval":null,"links":[],"mappings":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"options":{"showHeader":true},"repeat":null,"repeatDirection":null,"span":6,"targets":[{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"group(table_info{job=~\"$job\",instance=~\"$node\"}) by (table_id, table_name, table_type, materialized_view_id, fragment_id, compaction_group_id)","format":"table","hide":false,"instant":true,"interval":"","intervalFactor":2,"legendFormat":"","metric":"","query":"group(table_info{job=~\"$job\",instance=~\"$node\"}) by (table_id, table_name, table_type, materialized_view_id, fragment_id, compaction_group_id)","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"State Table Info","transformations":[{"id":"organize","options":{"excludeByName":{"Time":true,"Value":true},"indexByName":{"compaction_group_id":5,"fragment_id":4,"materialized_view_id":3,"table_id":0,"table_name":1,"table_type":2}}}],"transparent":false,"type":"table"},{"cacheTimeout":null,"color":{"mode":"thresholds"},"columns":[],"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"description":"Actor count per compute 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Check 'Hummock Manager' and `Compaction` section in dev dashboard and take care of the type of 'Compaction Success Count', whether the number of trivial-move tasks spiking.\n            - Abnormal Pending Event Number: the number of pending events is too large, exceeding the expected 10000000. Check 'Hummock Write' section in dev dashboard and take care of the 'Event handle latency', whether the time consumed exceeds the barrier latency.\n            - Abnormal Object Storage Failure: the number of object storage failures is too large, exceeding the expected 50. Check 'Object Storage' section in dev dashboard and take care of the 'Object Storage Failure Rate', whether the rate is too high.\n            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