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Client","transformations":[],"transparent":false,"type":"row"}],"refresh":"10s","rows":[],"schemaVersion":12,"sharedCrosshair":true,"style":"dark","tags":["risingwave"],"templating":{"list":[]},"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":"Ecy3uV1nz","version":0} diff --git a/grafana/README.md b/grafana/README.md index 6c87f4a7912d2..4b69c8fc12044 100644 --- a/grafana/README.md +++ b/grafana/README.md @@ -14,7 +14,7 @@ source venv/bin/activate pip install -r requirements.txt ``` -And don't forget to include the generated `risingwave-dashboard.json` in the commit. +And don't forget to include the generated `risingwave--dashboard.json` in the commit. ## Generate Dashboard diff --git a/grafana/common.py b/grafana/common.py new file mode 100644 index 0000000000000..97eb9d1f4c154 --- /dev/null +++ b/grafana/common.py @@ -0,0 +1,508 @@ +from grafanalib.core import Dashboard, TimeSeries, Target, GridPos, RowPanel, Time, Templating +import logging +import os + +# We use DASHBOARD_NAMESPACE_ENABLED env variable to indicate whether to add +# a filter for the namespace field in the prometheus metric. +NAMESPACE_FILTER_ENABLED = "DASHBOARD_NAMESPACE_FILTER_ENABLED" +# We use RISINGWAVE_NAME_FILTER_ENABLED env variable to indicate whether to add +# a filter for the namespace_filter field in the prometheus metric. +RISINGWAVE_NAME_FILTER_ENABLED = "DASHBOARD_RISINGWAVE_NAME_FILTER_ENABLED" +# We use DASHBOARD_SOURCE_UID env variable to pass custom source uid +SOURCE_UID = "DASHBOARD_SOURCE_UID" +# We use DASHBOARD_UID env variable to pass custom dashboard uid +DASHBOARD_UID = "DASHBOARD_UID" +# We use DASHBOARD_VERSION env variable to pass custom version +DASHBOARD_VERSION = "DASHBOARD_VERSION" + +namespace_filter_enabled = os.environ.get( + NAMESPACE_FILTER_ENABLED, "") == "true" +if namespace_filter_enabled: + print("Enable filter for namespace field in the generated prometheus query") +risingwave_name_filter_enabled = os.environ.get( + RISINGWAVE_NAME_FILTER_ENABLED, "") == "true" +if risingwave_name_filter_enabled: + print("Enable filter for namespace_filter field in the generated prometheus query") + +templating = Templating() +if namespace_filter_enabled: + templating = Templating( + list=[ + { + "definition": "label_values(up{risingwave_name=~\".+\"}, namespace)", + "description": "Kubernetes namespace.", + "hide": 0, + "includeAll": False, + "label": "Namespace", + "multi": True, + "name": "namespace", + "options": [], + "query": { + "query": "label_values(up{risingwave_name=~\".+\"}, namespace)", + "refId": "StandardVariableQuery" + }, + "refresh": 2, + "regex": "", + "skipUrlSync": False, + "sort": 0, + "type": "query" + } + ] + ) + +class Layout: + + def __init__(self): + self.x = 0 + self.y = 0 + self.w = 0 + self.h = 0 + + def next_row(self): + self.y += self.h + self.x = 0 + self.w = 24 + self.h = 1 + (x, y) = (self.x, self.y) + return GridPos(h=1, w=24, x=x, y=y) + + def next_half_width_graph(self): + if self.x + self.w > 24 - 12: + self.y += self.h + self.x = 0 + else: + self.x += self.w + (x, y) = (self.x, self.y) + self.h = 8 + self.w = 12 + return GridPos(h=8, w=12, x=x, y=y) + + def next_one_third_width_graph(self): + if self.x + self.w > 24 - 8: + self.y += self.h + self.x = 0 + else: + self.x += self.w + (x, y) = (self.x, self.y) + self.h = 8 + self.w = 8 + return GridPos(h=8, w=8, x=x, y=y) + + +class Panels: + + def __init__(self, datasource): + self.layout = Layout() + self.datasource = datasource + + def row( + self, + title, + ): + gridPos = self.layout.next_row() + return RowPanel(title=title, gridPos=gridPos) + + def row_collapsed(self, title, panels): + gridPos = self.layout.next_row() + return RowPanel(title=title, + gridPos=gridPos, + collapsed=True, + panels=panels) + + def target(self, expr, legendFormat, hide=False): + return Target(expr=expr, + legendFormat=legendFormat, + datasource=self.datasource, + hide=hide) + + def timeseries(self, title, description, targets): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + fillOpacity=10, + ) + + def timeseries_count(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_percentage(self, + title, + description, + targets, + legendCols=["mean"]): + # Percentage should fall into 0.0-1.0 + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="percentunit", + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_latency(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="s", + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_actor_latency(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="s", + fillOpacity=0, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_actor_latency_small(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_one_third_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="s", + fillOpacity=0, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_query_per_sec(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="Qps", + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_bytes_per_sec(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="Bps", + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_bytes(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="bytes", + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_row(self, title, description, targets, legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="row", + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_ms(self, title, description, targets, legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_kilobytes(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="kbytes", + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_dollar(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="$", + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_ops(self, title, description, targets, legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="ops", + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_actor_ops(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="ops", + fillOpacity=0, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_actor_ops_small(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_one_third_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="ops", + fillOpacity=0, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_rowsps(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="rows/s", + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_bytesps(self, + title, + description, + targets, + legendCols=["mean"]): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="MB/s", + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + legendCalcs=legendCols, + ) + + def timeseries_actor_rowsps(self, title, description, targets): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="rows/s", + fillOpacity=0, + legendDisplayMode="table", + legendPlacement="right", + ) + + def timeseries_memory(self, title, description, targets): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="bytes", + fillOpacity=10, + ) + + def timeseries_cpu(self, title, description, targets): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="percentunit", + fillOpacity=10, + ) + + def timeseries_latency_small(self, title, description, targets): + gridPos = self.layout.next_one_third_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + unit="s", + fillOpacity=10, + ) + + def timeseries_id(self, title, description, targets): + gridPos = self.layout.next_half_width_graph() + return TimeSeries( + title=title, + description=description, + targets=targets, + gridPos=gridPos, + fillOpacity=10, + legendDisplayMode="table", + legendPlacement="right", + ) + + def sub_panel(self): + return Panels(self.datasource) + + + + +def metric(name, filter=None): + filters = [filter] if filter else [] + if namespace_filter_enabled: + filters.append("namespace=~\"$namespace\"") + if risingwave_name_filter_enabled: + filters.append("risingwave_name=~\"$instance\"") + if filters: + return f"{name}{{{','.join(filters)}}}" + else: + return name + + +def quantile(f, percentiles): + quantile_map = { + "60": ["0.6", "60"], + "50": ["0.5", "50"], + "90": ["0.9", "90"], + "99": ["0.99", "99"], + "999": ["0.999", "999"], + "max": ["1.0", "max"], + } + return list( + map(lambda p: f(quantile_map[str(p)][0], quantile_map[str(p)][1]), + percentiles)) \ No newline at end of file diff --git a/grafana/generate.sh b/grafana/generate.sh index 735b4e2d1d5b4..18e34b9675a7b 100755 --- a/grafana/generate.sh +++ b/grafana/generate.sh @@ -2,5 +2,8 @@ set -euo pipefail -generate-dashboard -o risingwave-dashboard.gen.json risingwave-dashboard.dashboard.py -jq -c . risingwave-dashboard.gen.json > risingwave-dashboard.json +for dashboard_name in "risingwave-dev-dashboard" "risingwave-user-dashboard"; do + generate-dashboard -o $dashboard_name.gen.json $dashboard_name.dashboard.py + jq -c . $dashboard_name.gen.json > $dashboard_name.json + cp $dashboard_name.json ../docker/dashboards/ +done \ No newline at end of file diff --git a/grafana/risingwave-dashboard.json b/grafana/risingwave-dashboard.json deleted file mode 100644 index 7d447b0e66a11..0000000000000 --- a/grafana/risingwave-dashboard.json +++ /dev/null @@ -1 +0,0 @@ -{"__inputs":[],"annotations":{"list":[]},"description":"RisingWave Dashboard","editable":true,"gnetId":null,"graphTooltip":0,"hideControls":false,"id":null,"links":[],"panels":[{"cacheTimeout":null,"collapsed":false,"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":[],"repeat":null,"repeatDirection":null,"span":null,"targets":[],"timeFrom":null,"timeShift":null,"title":"Cluster Node","transformations":[],"transparent":false,"type":"row"},{"cacheTimeout":null,"datasource":null,"description":"The number of each type of RisingWave components 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grafana/risingwave-dashboard.dashboard.py rename to grafana/risingwave-dev-dashboard.dashboard.py index c650215274d7f..8c87c918d33fc 100644 --- a/grafana/risingwave-dashboard.dashboard.py +++ b/grafana/risingwave-dev-dashboard.dashboard.py @@ -1,497 +1,19 @@ from grafanalib.core import Dashboard, TimeSeries, Target, GridPos, RowPanel, Time, Templating import logging import os +import sys +p = os.path.dirname(__file__) +sys.path.append(p) +from common import * -# We use DASHBOARD_NAMESPACE_ENABLED env variable to indicate whether to add -# a filter for the namespace field in the prometheus metric. -NAMESPACE_FILTER_ENABLED = "DASHBOARD_NAMESPACE_FILTER_ENABLED" -# We use RISINGWAVE_NAME_FILTER_ENABLED env variable to indicate whether to add -# a filter for the namespace_filter field in the prometheus metric. -RISINGWAVE_NAME_FILTER_ENABLED = "DASHBOARD_RISINGWAVE_NAME_FILTER_ENABLED" -# We use DASHBOARD_SOURCE_UID env variable to pass custom source uid -SOURCE_UID = "DASHBOARD_SOURCE_UID" -# We use DASHBOARD_UID env variable to pass custom dashboard uid -DASHBOARD_UID = "DASHBOARD_UID" -# We use DASHBOARD_VERSION env variable to pass custom version -DASHBOARD_VERSION = "DASHBOARD_VERSION" - -namespace_filter_enabled = os.environ.get( - NAMESPACE_FILTER_ENABLED, "") == "true" -if namespace_filter_enabled: - print("Enable filter for namespace field in the generated prometheus query") -risingwave_name_filter_enabled = os.environ.get( - RISINGWAVE_NAME_FILTER_ENABLED, "") == "true" -if risingwave_name_filter_enabled: - print("Enable filter for namespace_filter field in the generated prometheus query") source_uid = os.environ.get(SOURCE_UID, "risedev-prometheus") dashboard_uid = os.environ.get(DASHBOARD_UID, "Ecy3uV1nz") dashboard_version = int(os.environ.get(DASHBOARD_VERSION, "0")) - - datasource = {"type": "prometheus", "uid": f"{source_uid}"} - -class Layout: - - def __init__(self): - self.x = 0 - self.y = 0 - self.w = 0 - self.h = 0 - - def next_row(self): - self.y += self.h - self.x = 0 - self.w = 24 - self.h = 1 - (x, y) = (self.x, self.y) - return GridPos(h=1, w=24, x=x, y=y) - - def next_half_width_graph(self): - if self.x + self.w > 24 - 12: - self.y += self.h - self.x = 0 - else: - self.x += self.w - (x, y) = (self.x, self.y) - self.h = 8 - self.w = 12 - return GridPos(h=8, w=12, x=x, y=y) - - def next_one_third_width_graph(self): - if self.x + self.w > 24 - 8: - self.y += self.h - self.x = 0 - else: - self.x += self.w - (x, y) = (self.x, self.y) - self.h = 8 - self.w = 8 - return GridPos(h=8, w=8, x=x, y=y) - - -class Panels: - - def __init__(self, datasource): - self.layout = Layout() - self.datasource = datasource - - def row( - self, - title, - ): - gridPos = self.layout.next_row() - return RowPanel(title=title, gridPos=gridPos) - - def row_collapsed(self, title, panels): - gridPos = self.layout.next_row() - return RowPanel(title=title, - gridPos=gridPos, - collapsed=True, - panels=panels) - - def target(self, expr, legendFormat, hide=False): - return Target(expr=expr, - legendFormat=legendFormat, - datasource=self.datasource, - hide=hide) - - def timeseries(self, title, description, targets): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - fillOpacity=10, - ) - - def timeseries_count(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_percentage(self, - title, - description, - targets, - legendCols=["mean"]): - # Percentage should fall into 0.0-1.0 - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="percentunit", - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_latency(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="s", - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_actor_latency(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="s", - fillOpacity=0, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_actor_latency_small(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_one_third_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="s", - fillOpacity=0, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_query_per_sec(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="Qps", - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_bytes_per_sec(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="Bps", - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_bytes(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="bytes", - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_row(self, title, description, targets, legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="row", - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_ms(self, title, description, targets, legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_kilobytes(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="kbytes", - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_dollar(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="$", - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_ops(self, title, description, targets, legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="ops", - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_actor_ops(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="ops", - fillOpacity=0, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_actor_ops_small(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_one_third_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="ops", - fillOpacity=0, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_rowsps(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="rows/s", - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_bytesps(self, - title, - description, - targets, - legendCols=["mean"]): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="MB/s", - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - legendCalcs=legendCols, - ) - - def timeseries_actor_rowsps(self, title, description, targets): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="rows/s", - fillOpacity=0, - legendDisplayMode="table", - legendPlacement="right", - ) - - def timeseries_memory(self, title, description, targets): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="bytes", - fillOpacity=10, - ) - - def timeseries_cpu(self, title, description, targets): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="percentunit", - fillOpacity=10, - ) - - def timeseries_latency_small(self, title, description, targets): - gridPos = self.layout.next_one_third_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - unit="s", - fillOpacity=10, - ) - - def timeseries_id(self, title, description, targets): - gridPos = self.layout.next_half_width_graph() - return TimeSeries( - title=title, - description=description, - targets=targets, - gridPos=gridPos, - fillOpacity=10, - legendDisplayMode="table", - legendPlacement="right", - ) - - def sub_panel(self): - return Panels(self.datasource) - - panels = Panels(datasource) - logging.basicConfig(level=logging.WARN) - -def metric(name, filter=None): - filters = [filter] if filter else [] - if namespace_filter_enabled: - filters.append("namespace=~\"$namespace\"") - if risingwave_name_filter_enabled: - filters.append("risingwave_name=~\"$instance\"") - if filters: - return f"{name}{{{','.join(filters)}}}" - else: - return name - - -def quantile(f, percentiles): - quantile_map = { - "60": ["0.6", "60"], - "50": ["0.5", "50"], - "90": ["0.9", "90"], - "99": ["0.99", "99"], - "999": ["0.999", "999"], - "max": ["1.0", "max"], - } - return list( - map(lambda p: f(quantile_map[str(p)][0], quantile_map[str(p)][1]), - percentiles)) - - def section_cluster_node(panels): return [ panels.row("Cluster Node"), @@ -2761,8 +2283,8 @@ def section_connector_node(outer_panels): ) dashboard = Dashboard( - title="risingwave_dashboard", - description="RisingWave Dashboard", + title="risingwave_dev_dashboard", + description="RisingWave Dev Dashboard", tags=["risingwave"], 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streaming","transformations":[],"transparent":false,"type":"timeseries"},{"cacheTimeout":null,"datasource":null,"description":"","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":"bytes"},"overrides":[]},"gridPos":{"h":8,"w":12,"x":12,"y":16},"height":null,"hideTimeOverride":false,"id":180,"interval":null,"links":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"options":{"legend":{"calcs":[],"displayMode":"list","placement":"bottom"},"tooltip":{"mode":"single"}},"repeat":null,"repeatDirection":null,"span":null,"targets":[{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"batch_total_mem_usage","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"","metric":"","query":"batch_total_mem_usage","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"The memory allocated by batch","transformations":[],"transparent":false,"type":"timeseries"}],"repeat":null,"repeatDirection":null,"span":null,"targets":[],"timeFrom":null,"timeShift":null,"title":"Memory manager","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":187},"height":null,"hideTimeOverride":false,"id":181,"interval":null,"links":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"panels":[{"cacheTimeout":null,"datasource":null,"description":"","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":"rows/s"},"overrides":[]},"gridPos":{"h":8,"w":12,"x":0,"y":0},"height":null,"hideTimeOverride":false,"id":182,"interval":null,"links":[],"maxDataPoints":100,"maxPerRow":null,"minSpan":null,"options":{"legend":{"calcs":["mean"],"displayMode":"table","placement":"right"},"tooltip":{"mode":"single"}},"repeat":null,"repeatDirection":null,"span":null,"targets":[{"datasource":{"type":"prometheus","uid":"risedev-prometheus"},"expr":"rate(connector_source_rows_received[$__interval])","format":"time_series","hide":false,"instant":false,"interval":"","intervalFactor":2,"legendFormat":"{{source_type}} @ {{source_id}}","metric":"","query":"rate(connector_source_rows_received[$__interval])","refId":"","step":10,"target":""}],"timeFrom":null,"timeShift":null,"title":"Connector Source Throughput(rows)","transformations":[],"transparent":false,"type":"timeseries"}],"repeat":null,"repeatDirection":null,"span":null,"targets":[],"timeFrom":null,"timeShift":null,"title":"Connector Node","transformations":[],"transparent":false,"type":"row"}],"refresh":"10s","rows":[],"schemaVersion":12,"sharedCrosshair":true,"style":"dark","tags":["risingwave"],"templating":{"list":[]},"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 new file mode 100644 index 0000000000000..934aaa342e5a2 --- /dev/null +++ b/grafana/risingwave-user-dashboard.dashboard.py @@ -0,0 +1,537 @@ +from grafanalib.core import Dashboard, TimeSeries, Target, GridPos, RowPanel, Time, Templating +import logging +import os +import sys +p = os.path.dirname(__file__) +sys.path.append(p) +from common import * + +source_uid = os.environ.get(SOURCE_UID, "risedev-prometheus") +dashboard_uid = os.environ.get(DASHBOARD_UID, "Fcy3uV1nz") +dashboard_version = int(os.environ.get(DASHBOARD_VERSION, "0")) +datasource = {"type": "prometheus", "uid": f"{source_uid}"} + +panels = Panels(datasource) +logging.basicConfig(level=logging.WARN) + + +def section_overview(panels): + return [ + panels.row("Overview"), + panels.timeseries_rowsps( + "Aggregated Source Throughput(rows/s)", + "The figure shows the number of rows read by each source per second.", + [ + panels.target( + f"sum(rate({metric('stream_source_output_rows_counts')}[$__rate_interval])) by (source_name)", + "{{source_name}}", + ), + ], + ), + panels.timeseries_bytesps( + "Aggregated Source Throughput(MB/s)", + "The figure shows the number of bytes read by each source per second.", + [ + panels.target( + f"(sum by (source_id)(rate({metric('partition_input_bytes')}[$__rate_interval])))/(1000*1000)", + "source_id {{source_id}}", + ) + ], + ), + panels.timeseries_latency( + "Barrier Latency", + "The time that the data between two consecutive barriers gets fully processed, i.e. the computation " + "results are made durable into materialized views or sink to external systems. This metric shows to users " + "the freshness of materialized views.", + quantile( + lambda quantile, legend: panels.target( + f"histogram_quantile({quantile}, sum(rate({metric('meta_barrier_duration_seconds_bucket')}[$__rate_interval])) by (le))", + f"barrier_latency_p{legend}", + ), + [50, 99], + ) + [ + panels.target( + f"rate({metric('meta_barrier_duration_seconds_sum')}[$__rate_interval]) / rate({metric('meta_barrier_duration_seconds_count')}[$__rate_interval])", + "barrier_latency_avg", + ), + ], + ), + panels.timeseries_count( + "Errors by Type", + "Errors in the system group by type", + [ + panels.target( + f"sum({metric('user_compute_error_count')}) by (error_type, error_msg, fragment_id, executor_name)", + "compute error {{error_type}}: {{error_msg}} ({{executor_name}}: fragment_id={{fragment_id}})", + ), + panels.target( + f"sum({metric('user_source_error_count')}) by (error_type, error_msg, fragment_id, table_id, executor_name)", + "source error {{error_type}}: {{error_msg}} ({{executor_name}}: table_id={{table_id}}, fragment_id={{fragment_id}})", + ), + panels.target( + f"sum(rate({metric('object_store_failure_count')}[$__rate_interval])) by (instance, job, type)", + "remote storage error {{type}}: {{job}} @ {{instance}}", + ) + ], + ), + panels.timeseries_query_per_sec( + "Batch Query QPS", + "", + [ + panels.target( + f"rate({metric('frontend_query_counter_local_execution')}[$__rate_interval])", + "Local mode", + ), + panels.target( + f"rate({metric('distributed_completed_query_counter')}[$__rate_interval])", + "Distributed mode", + ), + ], + ), + panels.timeseries_count( + "Node Count", + "The number of each type of RisingWave components alive.", + [ + panels.target(f"sum({metric('worker_num')}) by (worker_type)", + "{{worker_type}}") + ], + ["last"], + ), + ] + + +def section_cpu(outer_panels): + panels = outer_panels.sub_panel() + return [ + outer_panels.row_collapsed( + "CPU", + [ + panels.timeseries_cpu( + "Node CPU Usage", + "The CPU usage of each RisingWave component.", + [ + panels.target( + f"sum(rate({metric('process_cpu_seconds_total')}[$__rate_interval])) by (instance)", + "{{instance}}", + ), + ], + ), + panels.timeseries_count( + "Node CPU Core Number", + "Number of CPU cores per RisingWave component.", + [ + panels.target( + f"avg({metric('process_cpu_core_num')}) by (instance)", + "{{instance}}", + ), + ], + ), + ] + ) + ] + + +def section_memory(outer_panels): + panels = outer_panels.sub_panel() + return [ + outer_panels.row_collapsed( + "Memory", + [ + panels.timeseries_memory( + "Node Memory", + "The memory usage of each RisingWave component.", + [ + panels.target( + f"avg({metric('process_resident_memory_bytes')}) by (job,instance)", + "{{job}} @ {{instance}}", + ) + ], + ), + panels.timeseries_memory( + "Memory Usage (Total)", + "", + [ + panels.target( + f"sum({metric('stream_total_mem_usage')}) by (instance)", + "streaming @ {{instance}}", + ), + panels.target( + f"sum({metric('batch_total_mem_usage')}) by (instance)", + "batch @ {{instance}}", + ), + panels.target( + f"sum({metric('state_store_meta_cache_size')}) by (instance) + " + + f"sum({metric('state_store_block_cache_size')}) by (instance) + " + + f"sum({metric('state_store_limit_memory_size')}) by (instance)", + "storage @ {{instance}}", + ), + ], + ), + panels.timeseries_bytes( + "Memory Usage (Detailed)", + "", + [ + panels.target( + "rate(actor_memory_usage[$__rate_interval])", + "streaming actor - {{actor_id}}", + ), + panels.target( + f"sum({metric('state_store_meta_cache_size')}) by (job,instance)", + "storage meta cache - {{job}} @ {{instance}}", + ), + panels.target( + f"sum({metric('state_store_block_cache_size')}) by (job,instance)", + "storage block cache - {{job}} @ {{instance}}", + ), + panels.target( + f"sum({metric('state_store_limit_memory_size')}) by (job,instance)", + "storage write buffer - {{job}} @ {{instance}}", + ), + ], + ), + panels.timeseries_actor_ops( + "Executor Cache", + "Executor cache statistics", + [ + panels.target( + f"rate({metric('stream_join_lookup_miss_count')}[$__rate_interval])", + "Join - cache miss {{actor_id}} {{side}}", + ), + panels.target( + f"rate({metric('stream_join_lookup_total_count')}[$__rate_interval])", + "Join - total lookups {{actor_id}} {{side}}", + ), + panels.target( + f"rate({metric('stream_agg_lookup_miss_count')}[$__rate_interval])", + "Agg - cache miss {{actor_id}}", + ), + panels.target( + f"rate({metric('stream_agg_lookup_total_count')}[$__rate_interval])", + "Agg - total lookups {{actor_id}}", + ), + ], + ), + panels.timeseries_ops( + "Storage Cache", + "Storage cache statistics", + [ + panels.target( + f"sum(rate({metric('state_store_sst_store_block_request_counts')}[$__rate_interval])) by (job, instance, table_id, type)", + "memory cache - {{table_id}} @ {{type}} @ {{job}} @ {{instance}}", + ), + panels.target( + f"sum(rate({metric('state_store_read_req_check_bloom_filter_counts')}[$__rate_interval])) by (job,instance,table_id)", + "bloom filter total - {{table_id}} @ {{job}} @ {{instance}}", + ), + panels.target( + f"sum(rate({metric('state_store_read_req_positive_but_non_exist_counts')}[$__rate_interval])) by (job,instance,table_id)", + "bloom filter false positive - {{table_id}} @ {{job}} @ {{instance}}", + ), + panels.target( + f"sum(rate({metric('file_cache_latency_count')}[$__rate_interval])) by (op, instance)", + "file cache {{op}} @ {{instance}}", + ), + panels.target( + f"sum(rate({metric('file_cache_miss')}[$__rate_interval])) by (instance)", + "file cache miss @ {{instance}}", + ), + ], + ), + ] + ) + ] + + +def section_network(outer_panels): + panels = outer_panels.sub_panel() + return [ + outer_panels.row_collapsed( + "Network", + [ + panels.timeseries_bytes_per_sec( + "Streming Remote Exchange (Bytes/s)", + "Send/Recv throughput per node for streaming exchange", + [ + panels.target( + f"sum(rate({metric('stream_exchange_frag_send_size')}[$__rate_interval])) by (instance)", + "Send @ {{instance}}", + ), + panels.target( + f"sum(rate({metric('stream_exchange_frag_recv_size')}[$__rate_interval])) by (instance)", + "Recv @ {{instance}}", + ), + ], + ), + panels.timeseries_bytes_per_sec( + "Storage Remote I/O (Bytes/s)", + "The remote storage read/write throughput per node", + [ + panels.target( + f"sum(rate({metric('object_store_read_bytes')}[$__rate_interval])) by (instance)", + "read - {{instance}}", + ), + panels.target( + f"sum(rate({metric('object_store_write_bytes')}[$__rate_interval])) by (instance)", + "write - {{instance}}", + ), + ], + ), + panels.timeseries_row( + "Batch Exchange Recv (Rows/s)", + "", + [ + panels.target( + f"{metric('batch_task_exchange_recv_row_number')}", + "{{query_id}} : {{source_stage_id}}.{{source_task_id}} -> {{target_stage_id}}.{{target_task_id}}", + ), + ], + ), + ] + ) + ] + + +def section_storage(outer_panels): + panels = outer_panels.sub_panel() + return [ + outer_panels.row_collapsed( + "Storage", + [ + panels.timeseries_bytes( + "Object Size", + """ + Objects are classified into 3 groups: + - not referenced by versions: these object are being deleted from object store. + - referenced by non-current versions: these objects are stale (not in the latest version), but those old versions may still be in use (e.g. long-running pinning). Thus those objects cannot be deleted at the moment. + - referenced by current version: these objects are in the latest version. + """, + [ + panels.target(f"{metric('storage_stale_object_size')}", + "not referenced by versions"), + panels.target(f"{metric('storage_old_version_object_size')}", + "referenced by non-current versions"), + panels.target(f"{metric('storage_current_version_object_size')}", + "referenced by current version"), + ], + ), + panels.timeseries_count( + "Object Total Number", + """ + Objects are classified into 3 groups: + - not referenced by versions: these object are being deleted from object store. + - referenced by non-current versions: these objects are stale (not in the latest version), but those old versions may still be in use (e.g. long-running pinning). Thus those objects cannot be deleted at the moment. + - referenced by current version: these objects are in the latest version. + """, + [ + panels.target(f"{metric('storage_stale_object_count')}", + "not referenced by versions"), + panels.target(f"{metric('storage_old_version_object_count')}", + "referenced by non-current versions"), + panels.target(f"{metric('storage_current_version_object_count')}", + "referenced by current version"), + ], + ), + panels.timeseries_bytes( + "Write Bytes", + "The number of bytes that have been written by compaction." + "Flush refers to the process of compacting Memtables to SSTables at Level 0." + "Compaction refers to the process of compacting SSTables at one level to another level.", + [ + panels.target( + f"sum({metric('storage_level_compact_write')}) by (job) > 0", + "Compaction - {{job}}", + ), + panels.target( + f"sum({metric('compactor_write_build_l0_bytes')}) by (job) > 0", + "Flush - {{job}}", + ), + ], + ), + panels.timeseries_bytes_per_sec( + "Storage Remote I/O (Bytes/s)", + "The remote storage read/write throughput", + [ + panels.target( + f"sum(rate({metric('object_store_read_bytes')}[$__rate_interval])) by (job)", + "read - {{job}}", + ), + panels.target( + f"sum(rate({metric('object_store_write_bytes')}[$__rate_interval])) by (job)", + "write - {{job}}", + ), + ], + ), + panels.timeseries_bytes( + "Checkpoint Size", + "Size statistics for checkpoint", + [ + *quantile( + lambda quantile, legend: panels.target( + f"histogram_quantile({quantile}, sum(rate({metric('state_store_sync_size_bucket')}[$__rate_interval])) by (le, job))", + f"p{legend}" + " - {{job}}", + ), + [50, 99], + ), + panels.target( + f"sum by(le, job) (rate({metric('state_store_sync_size_sum')}[$__rate_interval])) / sum by(le, job) (rate({metric('state_store_sync_size_count')}[$__rate_interval]))", + "avg - {{job}}", + ), + ], + ), + ] + ) + ] + + +def section_streaming(outer_panels): + panels = outer_panels.sub_panel() + return [ + outer_panels.row_collapsed( + "Streaming", + [ + panels.timeseries_rowsps( + "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')}[$__rate_interval])", + "source={{source_name}} {{source_id}} @ {{instance}}", + ), + ], + ), + panels.timeseries_bytesps( + "Source Throughput(MB/s)", + "The figure shows the number of bytes read by each source per second.", + [ + panels.target( + f"(sum by (source_id)(rate({metric('partition_input_bytes')}[$__rate_interval])))/(1000*1000)", + "source={{source_id}}", + ) + ], + ), + panels.timeseries_rowsps( + "Backfill Throughput(rows)", + "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')}[$__rate_interval])", + "Read Snapshot - table_id={{table_id}} actor={{actor_id}} @ {{instance}}" + ), + panels.target( + f"rate({metric('stream_backfill_upstream_output_row_count')}[$__rate_interval])", + "Upstream - table_id={{table_id}} actor={{actor_id}} @ {{instance}}" + ), + ], + ), + panels.timeseries_percentage( + "Actor Backpressure", + "We first record the total blocking duration(ns) of output buffer of each actor. It shows how " + "much time it takes an actor to process a message, i.e. a barrier, a watermark or rows of data, " + "on average. Then we divide this duration by 1 second and show it as a percentage.", + [ + panels.target( + f"rate({metric('stream_actor_output_buffer_blocking_duration_ns')}[$__rate_interval]) / 1000000000", + "{{actor_id}}", + ), + ], + ), + ] + ) + ] + + +def section_batch(outer_panels): + panels = outer_panels.sub_panel() + return [ + outer_panels.row_collapsed( + "Batch", + [ + panels.timeseries_count( + "Running query in distributed execution mode", + "", + [ + panels.target(f"{metric('distributed_running_query_num')}", + "The number of running query in distributed execution mode"), + ], + ["last"], + ), + panels.timeseries_count( + "Rejected query in distributed execution mode", + "", + [ + panels.target(f"{metric('distributed_rejected_query_counter')}", + "The number of rejected query in distributed execution mode"), + ], + ["last"], + ), + panels.timeseries_count( + "Completed query in distributed execution mode", + "", + [ + panels.target(f"{metric('distributed_completed_query_counter')}", + "The number of completed query in distributed execution mode"), + ], + ["last"], + ), + panels.timeseries_latency( + "Query Latency in Distributed Execution Mode", + "", + [ + panels.target( + f"histogram_quantile(0.5, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, job, instance))", + "p50 - {{job}} @ {{instance}}", + ), + panels.target( + f"histogram_quantile(0.9, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, job, instance))", + "p90 - {{job}} @ {{instance}}", + ), + panels.target( + f"histogram_quantile(0.95, sum(rate({metric('distributed_query_latency_bucket')}[$__rate_interval])) by (le, job, instance))", + "p99 - {{job}} @ {{instance}}", + ), + ], + ), + panels.timeseries_latency( + "Query Latency in Local Execution Mode", + "", + [ + panels.target( + f"histogram_quantile(0.5, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, job, instance))", + "p50 - {{job}} @ {{instance}}", + ), + panels.target( + f"histogram_quantile(0.9, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, job, instance))", + "p90 - {{job}} @ {{instance}}", + ), + panels.target( + f"histogram_quantile(0.95, sum(rate({metric('frontend_latency_local_execution_bucket')}[$__rate_interval])) by (le, job, instance))", + "p99 - 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dashboard in "risingwave-user-dashboard.json" "risingwave-dev-dashboard.json"; do + payload="{\"dashboard\": $(jq . $dashboard), \"overwrite\": true}" + echo "$payload" > payload.txt + curl -X POST \ + -H 'Content-Type: application/json' \ + -d @payload.txt \ + "http://admin:admin@localhost:3001/api/dashboards/db" -echo "$payload" > payload.txt + rm payload.txt +done -curl -X POST \ - -H 'Content-Type: application/json' \ - -d @payload.txt \ - "http://admin:admin@localhost:3001/api/dashboards/db" -rm payload.txt \ No newline at end of file diff --git a/src/risedevtool/src/config_gen/grafana_gen.rs b/src/risedevtool/src/config_gen/grafana_gen.rs index eebd3a5673b52..6033501f0e1f0 100644 --- a/src/risedevtool/src/config_gen/grafana_gen.rs +++ b/src/risedevtool/src/config_gen/grafana_gen.rs @@ -87,14 +87,18 @@ datasources: )); }; - let filename = "risingwave-dashboard.json"; + let filenames = [ + "risingwave-user-dashboard.json", + "risingwave-dev-dashboard.json", + ]; let generate_path = generate_path.as_ref(); - let dashboard_path = Path::new(generate_path).join(filename); - fs_err::copy("grafana/risingwave-dashboard.json", dashboard_path)?; - + for filename in filenames { + let from = Path::new("grafana").join(filename); + let to = Path::new(generate_path).join(filename); + std::fs::copy(from, to)?; + } let grafana_read_path = grafana_read_path.as_ref(); - let dashboard_path = Path::new(grafana_read_path).join(filename); - let dashboard_path_str = dashboard_path + let dashboard_path_str = grafana_read_path .to_str() .ok_or_else(|| anyhow!("invalid string"))?;