TQL
TQL 关键字在 SQL 中执行 TQL 语言。TQL 是 Telemetry Query Language 的缩写，是 GreptimeDB 中对 Prometheus 的 PromQL 的扩展。
EVAL
Syntax
TQL [EVAL | EVALUATE] (start, end, step, [lookback]) expr
start, end 和 step 是查询参数，就像 Prometheus Query API 一样：
start:<rfc3339 | unix_timestamp | expression >: 查询的起始时间戳，范围中包含该值。end:<rfc3339 | unix_timestamp | expression>: 查询的截止时间戳，范围中包含该值。step:<duration | float>: 查询分辨率步长，采用duration格式或浮点秒数。lookback:<duration | float>: 查询评估的最大过去持续时间，默认 5 分钟，可选参数。
expr 是 TQL (PromQL) 的查询字符串。
示例
返回过去 5 分钟内 http_requests_total 指标的所有时间序列的每秒值：
TQL EVAL (1677057993, 1677058993, '1m')
rate(prometheus_http_requests_total{job="prometheus"}[5m]);
其查询结果和 SQL 查询结果类似。
start 和 end 还可以是可以被求值为常量的时间表达式，例如查询过去 3 个小时：
TQL EVAL (now() - interval '3' hours, now(), '1m')
sum by (namespace, pod) (
increase(kube_pod_container_status_restarts_total[10m:30s])
);
查询过去一天的数据：
TQL EVAL (
date_trunc('day', now() - interval '1' day),
date_trunc('day', now()),
'1m'
)
sum by (namespace) (
rate(http_requests_total[5m:30s])
);
EXPLAIN
EXPLAIN 展示特定 PromQL 查询的逻辑计划和执行计划，其语法如下：
TQL EXPLAIN expr;
例如，我们可以使用下方示例解释 PromQL sum by (instance) (rate(node_disk_written_bytes_total[2m])) > 50：
TQL EXPLAIN sum by (instance) (rate(node_disk_written_bytes_total[2m])) > 50;
注意该查询实际上没有被执行，所以 (start, end, step, [lookback]) 不是必需的，但你仍然可以像在 TQL EVAL 中一样提供这些参数：
TQL EXPLAIN (0, 100, '10s') sum by (instance) (rate(node_disk_written_bytes_total[2m])) > 50;
结果如下：
+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| plan_type | plan |
+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| logical_plan | Sort: node_disk_written_bytes_total.instance ASC NULLS LAST, node_disk_written_bytes_total.ts ASC NULLS LAST
Filter: SUM(prom_rate(ts_range,field,ts)) > Float64(50)
Aggregate: groupBy=[[node_disk_written_bytes_total.instance, node_disk_written_bytes_total.ts]], aggr=[[SUM(prom_rate(ts_range,field,ts))]]
Projection: node_disk_written_bytes_total.ts, prom_rate(ts_range, field, node_disk_written_bytes_total.ts) AS prom_rate(ts_range,field,ts), node_disk_written_bytes_total.instance
Filter: prom_rate(ts_range, field, node_disk_written_bytes_total.ts) IS NOT NULL
Projection: node_disk_written_bytes_total.ts, node_disk_written_bytes_total.instance, field, ts_range
PromRangeManipulate: req range=[0..0], interval=[300000], eval range=[120000], time index=[ts], values=["field"]
PromSeriesNormalize: offset=[0], time index=[ts], filter NaN: [true]
PromSeriesDivide: tags=["instance"]
Sort: node_disk_written_bytes_total.instance DESC NULLS LAST, node_disk_written_bytes_total.ts DESC NULLS LAST
TableScan: node_disk_written_bytes_total projection=[ts, instance, field], partial_filters=[ts >= TimestampMillisecond(-420000, None), ts <= TimestampMillisecond(300000, None)] |
| physical_plan | SortPreservingMergeExec: [instance@0 ASC NULLS LAST,ts@1 ASC NULLS LAST]
SortExec: expr=[instance@0 ASC NULLS LAST,ts@1 ASC NULLS LAST]
CoalesceBatchesExec: target_batch_size=8192
FilterExec: SUM(prom_rate(ts_range,field,ts))@2 > 50
AggregateExec: mode=FinalPartitioned, gby=[instance@0 as instance, ts@1 as ts], aggr=[SUM(prom_rate(ts_range,field,ts))]
CoalesceBatchesExec: target_batch_size=8192
RepartitionExec: partitioning=Hash([Column { name: "instance", index: 0 }, Column { name: "ts", index: 1 }], 32), input_partitions=32
AggregateExec: mode=Partial, gby=[instance@2 as instance, ts@0 as ts], aggr=[SUM(prom_rate(ts_range,field,ts))]
ProjectionExec: expr=[ts@0 as ts, prom_rate(ts_range@3, field@2, ts@0) as prom_rate(ts_range,field,ts), instance@1 as instance]
CoalesceBatchesExec: target_batch_size=8192
FilterExec: prom_rate(ts_range@3, field@2, ts@0) IS NOT NULL
ProjectionExec: expr=[ts@0 as ts, instance@1 as instance, field@2 as field, ts_range@3 as ts_range]
PromInstantManipulateExec: req range=[0..0], interval=[300000], eval range=[120000], time index=[ts]
PromSeriesNormalizeExec: offset=[0], time index=[ts], filter NaN: [true]
PromSeriesDivideExec: tags=["instance"]
RepartitionExec: partitioning=RoundRobinBatch(32), input_partitions=1
ExecutionPlan(PlaceHolder)
|
+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
ANALYZE
TQL 同样支持 ANALYZE 关键词来分析给定 PromQL 查询的执行，其语法如下：
TQL ANALYZE (start, end, step) expr;
例如：
TQL ANALYZE (0, 10, '5s') test;
得到结果：
+-------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| plan_type | plan |
+-------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Plan with Metrics | CoalescePartitionsExec, metrics=[output_rows=0, elapsed_compute=14.99µs]
PromInstantManipulateExec: range=[0..10000], lookback=[300000], interval=[5000], time index=[j], metrics=[output_rows=0, elapsed_compute=1.08µs]
PromSeriesNormalizeExec: offset=[0], time index=[j], filter NaN: [false], metrics=[output_rows=0, elapsed_compute=1.11µs]
PromSeriesDivideExec: tags=["k"], metrics=[output_rows=0, elapsed_compute=1.3µs]
RepartitionExec: partitioning=RoundRobinBatch(32), input_partitions=32, metrics=[send_time=32ns, repart_time=32ns, fetch_time=11.578016ms]
RepartitionExec: partitioning=RoundRobinBatch(32), input_partitions=1, metrics=[send_time=1ns, repart_time=1ns, fetch_time=21.07µs]
ExecutionPlan(PlaceHolder), metrics=[]
|
+-------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
使用 TQL ANALYZE VERBOSE 可以拿到查询执行时更详细的信息.
TQL ANALYZE VERBOSE (0, 10, '5s') test;