Repository: risingwavelabs/risingwave
File: grafana/dashboard/dev/streaming.py (Python)

Code before:
                                )
                            """,
                            "table_name={{table_name}} table_id={{table_id}}",
                        ),
                        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,
                        ),
                    ],
                ),
                panels.timeseries_epoch(
                    "Current Epoch of Materialize Views",
                    "The current epoch that the Materialize Executors are processing. If an MV's epoch is far behind the others, "
                    "it's very likely to be the performance bottleneck",
                    [
                        panels.target(
                            # Here we use `min` but actually no much difference. Any of the sampled `current_epoch` makes sense.
                            f"min({metric('stream_mview_current_epoch')} != 0) by (table_id) * on(table_id) group_left(table_name) group({metric('table_info')}) by (table_id, table_name)",
                            "{{table_id}} {{table_name}}",
                        ),
                    ],
                ),
                panels.timeseries_latency(
                    "Snapshot Backfill Lag",
                    "",
                    [
                        panels.target(
                            f"{metric('meta_snapshot_backfill_upstream_lag')} / (2^16) / 1000",
                            "lag @ {{table_id}}",
                        ),
                    ],
                ),
                panels.subheader(
                    "Barrier",
                    "More fine-grained barrier metrics here. Core metrics like **Barrier Number** are in **Cluster Essential** section.",
                    height=2.2,
                ),
                panels.timeseries_latency(
                    "Barrier Send Latency",
                    "The duration between the time point when the scheduled barrier needs to be sent and the time point when "
                    "the barrier gets actually sent to all the compute nodes. Developers can thus detect any internal "
                    "congestion.",
                    quantile(
                        lambda quantile, legend: panels.target(
                            f"histogram_quantile({quantile}, sum(rate({metric('meta_barrier_send_duration_seconds_bucket')}[$__rate_interval])) by (le, database_id))",
                            f"barrier_send_latency_p{legend}" + " {{database_id}}",
                        ),
                        [50, 90, 99, 999, "max"],
                    )
                    + [

Review comment (suggestion):
Inconsistent spelling: 'Materialize Executors' should be 'Materialized Executors' for consistency with standard terminology and other references in the codebase.
```suggestion
                    "The current epoch that the Materialized Executors or Sink Executor are processing. If an MV/Sink's epoch is far behind the others, "
```

Code after:
                                )
                            """,
                            "table_name={{table_name}} table_id={{table_id}}",
                        ),
                        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,
                        ),
                    ],
                ),
                panels.timeseries_epoch(
                    "Current Epoch of Materialize Views",
                    "The current epoch that the Materialize Executors are processing. If an MV's epoch is far behind the others, "
                    "it's very likely to be the performance bottleneck",
                    [
                        panels.target(
                            # Here we use `min` but actually no much difference. Any of the sampled `current_epoch` makes sense.
                            f"min({metric('stream_mview_current_epoch')} != 0) by (table_id) * on(table_id) group_left(table_name) group({metric('table_info')}) by (table_id, table_name)",
                            "{{table_id}} {{table_name}}",
                        ),
                    ],
                ),
                panels.timeseries_latency(
                    "Latency of Materialize Views & Sinks",
                    "The current epoch that the Materialize Executors or Sink Executor are processing. If an MV/Sink's epoch is far behind the others, "
                    "it's very likely to be the performance bottleneck",
                    [
                        panels.target(
                            # Here we use `min` but actually no much difference. Any of the sampled `current_epoch` makes sense.
                            f"max(timestamp({metric('stream_mview_current_epoch')}) - {epoch_to_unix_millis(metric('stream_mview_current_epoch'))}/1000) by (table_id) * on(table_id) group_left(table_name) group({metric('table_info')}) by (table_id, table_name)",
                            "{{table_id}} {{table_name}}",
                        ),
                        panels.target(
                            f"max(timestamp({metric('log_store_latest_read_epoch')}) - {epoch_to_unix_millis(metric('log_store_latest_read_epoch'))}/1000) by (sink_id, sink_name)",
                            "{{sink_id}} {{sink_name}} (output)",
                        ),
                        panels.target(
                            f"max(timestamp({metric('log_store_latest_write_epoch')}) - {epoch_to_unix_millis(metric('log_store_latest_write_epoch'))}/1000) by (sink_id, sink_name)",
                            "{{sink_id}} {{sink_name}} (enqueue)",
                        ),
                    ],
                ),
                panels.timeseries_latency(
                    "Snapshot Backfill Lag",
                    "",
                    [
                        panels.target(
                            f"{metric('meta_snapshot_backfill_upstream_lag')} / (2^16) / 1000",
                            "lag @ {{table_id}}",
                        ),