New in version 0.7.
Converts stat values extracted from statmetric messages (see Stat Accumulator Input) to circular buffer data and periodically emits messages containing this data to be graphed by a DashboardOutput. Note that this filter expects the stats data to be available in the message fields, so the StatAccumInput must be configured with emit_in_fields set to true for this filter to work correctly.
Title for the graph output generated by this filter.
The number of rows to store in our circular buffer. Each row represents one time interval.
The number of seconds in each circular buffer time interval.
Space separated list of stat names. Each specified stat will be expected to be found in the fields of the received statmetric messages, and will be extracted and inserted into its own column in the accumulated circular buffer.
Space separated list of header label names to use for the extracted stats. Must be in the same order as the specified stats. Any label longer than 15 characters will be truncated.
Anomaly detection configuration, see Anomaly Detection Module.
If preserve_data = true is set in the SandboxFilter configuration, then this value should be incremented every time any edits are made to your rows, sec_per_row, stats, or stat_labels values, or else Heka will fail to start because the preserved data will no longer match the filter’s data structure.
“sum” - The total is computed for the time/column (default). “min” - The smallest value is retained for the time/column. “max” - The largest value is retained for the time/column. “none” - No aggregation will be performed the column.
The unit of measure (maximum 7 characters). Alpha numeric, ‘/’, and ‘*’ characters are allowed everything else will be converted to underscores. i.e. KiB, Hz, m/s (default: count).
Example Heka Configuration
[stat-graph] type = "SandboxFilter" filename = "lua_filters/stat_graph.lua" ticker_interval = 10 preserve_data = true message_matcher = "Type == 'heka.statmetric'" [stat-graph.config] title = "Hits and Misses" rows = 1440 stat_aggregation = "none" stat_unit = "count" sec_per_row = 10 stats = "stats.counters.hits.count stats.counters.misses.count" stat_labels = "hits misses" anomaly_config = 'roc("Hits and Misses", 1, 15, 0, 1.5, true, false) roc("Hits and Misses", 2, 15, 0, 1.5, true, false)' preservation_version = 0