5143-Add Optimize Queries page with query analysis help (#5165)
* chore(test): Use my python client fork (pending approval) to allow custom headers. * feature(query): Add Optimize Queries page with query analysis help - Closes Client library query traces: Python #5143 - Dedicated and Clustered examples for enabling query tracing and extracting headers - System.queries table - Explain and Analyze - For now, skip tests for sample Flight responses until we add in code samples. * Update content/influxdb/clustered/query-data/execute-queries/optimize-queries.md Co-authored-by: Scott Anderson <sanderson@users.noreply.github.com> * Update content/influxdb/cloud-dedicated/query-data/execute-queries/optimize-queries.md Co-authored-by: Scott Anderson <sanderson@users.noreply.github.com> * Update content/influxdb/cloud-dedicated/query-data/execute-queries/optimize-queries.md Co-authored-by: Scott Anderson <sanderson@users.noreply.github.com> * Update content/influxdb/cloud-dedicated/query-data/execute-queries/optimize-queries.md Co-authored-by: Scott Anderson <sanderson@users.noreply.github.com> * Update content/influxdb/clustered/query-data/execute-queries/optimize-queries.md Co-authored-by: Scott Anderson <sanderson@users.noreply.github.com> * Update content/influxdb/clustered/query-data/execute-queries/optimize-queries.md Co-authored-by: Scott Anderson <sanderson@users.noreply.github.com> * Update content/influxdb/clustered/query-data/execute-queries/optimize-queries.md Co-authored-by: Scott Anderson <sanderson@users.noreply.github.com> * Update content/influxdb/clustered/query-data/execute-queries/optimize-queries.md Co-authored-by: Scott Anderson <sanderson@users.noreply.github.com> * Update content/influxdb/cloud-dedicated/query-data/execute-queries/optimize-queries.md Co-authored-by: Scott Anderson <sanderson@users.noreply.github.com> * Update content/influxdb/cloud-dedicated/query-data/execute-queries/optimize-queries.md Co-authored-by: Scott Anderson <sanderson@users.noreply.github.com> * feat(v3): influx-trace-id for dedicated, tracing not ready for clustered (Client library query traces: Python #5143) --------- Co-authored-by: Scott Anderson <sanderson@users.noreply.github.com>pull/5180/head^2
Jason Stirnaman
GitHub
parent
6be4bbd3bc
commit
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@ -25,6 +25,7 @@ related:
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- /influxdb/cloud-dedicated/query-data/sql/
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- /influxdb/cloud-dedicated/reference/influxql/
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- /influxdb/cloud-dedicated/reference/sql/
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- /influxdb/cloud-dedicated/query-data/execute-queries/troubleshoot/
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list_code_example: |
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```py
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@ -305,7 +306,7 @@ and specify the following arguments:
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#### Example {#execute-query-example}
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The following examples shows how to use SQL or InfluxQL to select all fields in a measurement, and then output the results formatted as a Markdown table.
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The following example shows how to use SQL or InfluxQL to select all fields in a measurement, and then use PyArrow functions to extract metadata and aggregate data.
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{{% code-tabs-wrapper %}}
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{{% code-tabs %}}
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@ -0,0 +1,442 @@
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---
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title: Optimize queries
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description: >
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Optimize your SQL and InfluxQL queries to improve performance and reduce their memory and compute (CPU) requirements.
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weight: 401
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menu:
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influxdb_cloud_dedicated:
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name: Optimize queries
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parent: Execute queries
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influxdb/cloud-dedicated/tags: [query, sql, influxql]
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related:
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- /influxdb/cloud-dedicated/query-data/sql/
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- /influxdb/cloud-dedicated/query-data/influxql/
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- /influxdb/cloud-dedicated/query-data/execute-queries/troubleshoot/
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- /influxdb/cloud-dedicated/reference/client-libraries/v3/
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---
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Use the following tools to help you identify performance bottlenecks and troubleshoot problems in queries:
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<!-- TOC -->
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- [EXPLAIN and ANALYZE](#explain-and-analyze)
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- [Enable trace logging](#enable-trace-logging)
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- [Avoid unnecessary tracing](#avoid-unnecessary-tracing)
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- [Syntax](#syntax)
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- [Example](#example)
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- [Tracing response header](#tracing-response-header)
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- [Trace response header syntax](#trace-response-header-syntax)
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- [Inspect Flight response headers](#inspect-flight-response-headers)
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- [Retrieve query information](#retrieve-query-information)
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<!-- /TOC -->
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## EXPLAIN and ANALYZE
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To view the query engine's execution plan and metrics for an SQL or InfluxQL query, prepend [`EXPLAIN`](/influxdb/cloud-dedicated/reference/sql/explain/) or [`EXPLAIN ANALYZE`](/influxdb/cloud-dedicated/reference/sql/explain/#explain-analyze) to the query.
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The report can reveal query bottlenecks such as a large number of table scans or parquet files, and can help triage the question, "Is the query slow due to the amount of work required or due to a problem with the schema, compactor, etc.?"
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The following example shows how to use the InfluxDB v3 Python client library and pandas to view `EXPLAIN` and `EXPLAIN ANALYZE` results for a query:
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<!-- Import for tests and hide from users.
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```python
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import os
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```
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-->
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{{% code-placeholders "DATABASE_(NAME|TOKEN)" %}}
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<!--pytest-codeblocks:cont-->
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```python
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from influxdb_client_3 import InfluxDBClient3
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import pandas as pd
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import tabulate # Required for pandas.to_markdown()
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# Instantiate an InfluxDB client.
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client = InfluxDBClient3(token = f"DATABASE_TOKEN",
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host = f"{{< influxdb/host >}}",
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database = f"DATABASE_NAME")
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sql_explain = '''EXPLAIN
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SELECT temp
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FROM home
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WHERE time >= now() - INTERVAL '90 days'
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AND room = 'Kitchen'
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ORDER BY time'''
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table = client.query(sql_explain)
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df = table.to_pandas()
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print(df.to_markdown(index=False))
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assert df.shape == (2, 2), f'Expect {df.shape} to have 2 columns, 2 rows'
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assert 'physical_plan' in df.plan_type.values, "Expect physical_plan"
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assert 'logical_plan' in df.plan_type.values, "Expect logical_plan"
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```
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{{< expand-wrapper >}}
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{{% expand "View EXPLAIN example results" %}}
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| plan_type | plan |
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|:--------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| logical_plan | Projection: home.temp |
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| | Sort: home.time ASC NULLS LAST |
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| | Projection: home.temp, home.time |
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| | TableScan: home projection=[room, temp, time], full_filters=[home.time >= TimestampNanosecond(1688676582918581320, None), home.room = Dictionary(Int32, Utf8("Kitchen"))] |
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| physical_plan | ProjectionExec: expr=[temp@0 as temp] |
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| | SortExec: expr=[time@1 ASC NULLS LAST] |
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| | EmptyExec: produce_one_row=false |
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{{% /expand %}}
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{{< /expand-wrapper >}}
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<!--pytest-codeblocks:cont-->
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```python
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sql_explain_analyze = '''EXPLAIN ANALYZE
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SELECT *
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FROM home
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WHERE time >= now() - INTERVAL '90 days'
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ORDER BY time'''
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table = client.query(sql_explain_analyze)
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df = table.to_pandas()
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print(df.to_markdown(index=False))
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assert df.shape == (1,2)
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assert 'Plan with Metrics' in df.plan_type.values, "Expect plan metrics"
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client.close()
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```
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{{% /code-placeholders %}}
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Replace the following:
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- {{% code-placeholder-key %}}`DATABASE_NAME`{{% /code-placeholder-key %}}: your {{% product-name %}} database
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- {{% code-placeholder-key %}}`DATABASE_TOKEN`{{% /code-placeholder-key %}}: a [database token](/influxdb/cloud-dedicated/admin/tokens/) with sufficient permissions to the specified database
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{{< expand-wrapper >}}
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{{% expand "View EXPLAIN ANALYZE example results" %}}
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| plan_type | plan |
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|:------------------|:-----------------------------------------------------------------------------------------------------------------------|
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| Plan with Metrics | ProjectionExec: expr=[temp@0 as temp], metrics=[output_rows=0, elapsed_compute=1ns] |
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| | SortExec: expr=[time@1 ASC NULLS LAST], metrics=[output_rows=0, elapsed_compute=1ns, spill_count=0, spilled_bytes=0] |
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| | EmptyExec: produce_one_row=false, metrics=[]
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{{% /expand %}}
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{{< /expand-wrapper >}}
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## Enable trace logging
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When you enable trace logging for a query, InfluxDB propagates your _trace ID_ through system processes and collects additional log information.
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InfluxDB Support can then use the trace ID that you provide to filter, collate, and analyze log information for the query run.
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The tracing system follows the [OpenTelemetry traces](https://opentelemetry.io/docs/concepts/signals/traces/) model for providing observability into a request.
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{{% warn %}}
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#### Avoid unnecessary tracing
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Only enable tracing for a query when you need to request troubleshooting help from InfluxDB Support.
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To manage resources, InfluxDB has an upper limit for the number of trace requests.
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Too many traces can cause InfluxDB to evict log information.
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{{% /warn %}}
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To enable tracing for a query, include the `influx-trace-id` header in your query request.
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### Syntax
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Use the following syntax for the `influx-trace-id` header:
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```http
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influx-trace-id: TRACE_ID:1112223334445:0:1
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```
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In the header value, replace the following:
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- `TRACE_ID`: a unique string, 8-16 bytes long, encoded as hexadecimal (32 maximum hex characters).
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The trace ID should uniquely identify the query run.
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- `:1112223334445:0:1`: InfluxDB constant values (required, but ignored)
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### Example
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The following examples show how to create and pass a trace ID to enable query tracing in InfluxDB:
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{{< tabs-wrapper >}}
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{{% tabs %}}
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[Python with FlightCallOptions](#)
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[Python with FlightClientMiddleware](#python-with-flightclientmiddleware)
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{{% /tabs %}}
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{{% tab-content %}}
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<!---- BEGIN PYTHON WITH FLIGHTCALLOPTIONS ---->
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Use the `InfluxDBClient3` InfluxDB Python client and pass the `headers` argument in the
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`query()` method.
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<!-- Import for tests and hide from users.
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```python
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import os
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```
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-->
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{{% code-placeholders "DATABASE_(NAME|TOKEN)|APP_REQUEST_ID" %}}
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<!--pytest-codeblocks:cont-->
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```python
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from influxdb_client_3 import InfluxDBClient3
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import secrets
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def use_flightcalloptions_trace_header():
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print('# Use FlightCallOptions to enable tracing.')
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client = InfluxDBClient3(token=f"DATABASE_TOKEN",
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host=f"{{< influxdb/host >}}",
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database=f"DATABASE_NAME")
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# Generate a trace ID for the query:
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# 1. Generate a random 8-byte value as bytes.
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# 2. Encode the value as hexadecimal.
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random_bytes = secrets.token_bytes(8)
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trace_id = random_bytes.hex()
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# Append required constants to the trace ID.
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trace_value = f"{trace_id}:1112223334445:0:1"
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# Encode the header key and value as bytes.
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# Create a list of header tuples.
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headers = [((b"influx-trace-id", trace_value.encode('utf-8')))]
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sql = "SELECT * FROM home WHERE time >= now() - INTERVAL '30 days'"
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influxql = "SELECT * FROM home WHERE time >= -90d"
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# Use the query() headers argument to pass the list as FlightCallOptions.
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client.query(sql, headers=headers)
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client.close()
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use_flightcalloptions_trace_header()
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```
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{{% /code-placeholders %}}
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<!---- END PYTHON WITH FLIGHTCALLOPTIONS ---->
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{{% /tab-content %}}
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{{% tab-content %}}
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<!---- BEGIN PYTHON WITH MIDDLEWARE ---->
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Use the `InfluxDBClient3` InfluxDB Python client and `flight.ClientMiddleware` to pass and inspect headers.
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### Tracing response header
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With tracing enabled and a valid trace ID in the request, InfluxDB's `DoGet` action response contains a header with the trace ID that you sent.
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#### Trace response header syntax
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```http
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trace-id: TRACE_ID
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```
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### Inspect Flight response headers
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To inspect Flight response headers when using a client library, pass a `FlightClientMiddleware` instance.
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that defines a middleware callback function for the `onHeadersReceived` event (the particular function name you use depends on the client library language).
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The following example uses Python client middleware that adds request headers and extracts the trace ID from the `DoGet` response headers:
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<!-- Import for tests and hide from users.
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```python
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import os
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```
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-->
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{{% code-placeholders "DATABASE_(NAME|TOKEN)|APP_REQUEST_ID" %}}
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<!--pytest-codeblocks:cont-->
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```python
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import pyarrow.flight as flight
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class TracingClientMiddleWareFactory(flight.ClientMiddleware):
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# Defines a custom middleware factory that returns a middleware instance.
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def __init__(self):
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self.request_headers = []
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self.response_headers = []
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self.traces = []
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def addRequestHeader(self, header):
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self.request_headers.append(header)
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def addResponseHeader(self, header):
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self.response_headers.append(header)
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def addTrace(self, traceid):
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self.traces.append(traceid)
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def createTrace(self, traceid):
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# Append InfluxDB constants to the trace ID.
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trace = f"{traceid}:1112223334445:0:1"
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# To the list of request headers,
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# add a tuple with the header key and value as bytes.
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self.addRequestHeader((b"influx-trace-id", trace.encode('utf-8')))
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def start_call(self, info):
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return TracingClientMiddleware(info.method, self)
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class TracingClientMiddleware(flight.ClientMiddleware):
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# Defines middleware with client event callback methods.
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def __init__(self, method, callback_obj):
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self._method = method
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self.callback = callback_obj
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def call_completed(self, exception):
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print('callback: call_completed')
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if(exception):
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print(f" ...with exception: {exception}")
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def sending_headers(self):
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print('callback: sending_headers: ', self.callback.request_headers)
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if len(self.callback.request_headers) > 0:
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return dict(self.callback.request_headers)
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def received_headers(self, headers):
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self.callback.addResponseHeader(headers)
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# For the DO_GET action, extract the trace ID from the response headers.
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if str(self._method) == "FlightMethod.DO_GET" and "trace-id" in headers:
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trace_id = headers["trace-id"][0]
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self.callback.addTrace(trace_id)
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from influxdb_client_3 import InfluxDBClient3
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import secrets
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def use_middleware_trace_header():
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print('# Use Flight client middleware to enable tracing.')
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# Instantiate the middleware.
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res = TracingClientMiddleWareFactory()
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# Instantiate the client, passing in the middleware instance that provides
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# event callbacks for the request.
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client = InfluxDBClient3(token=f"DATABASE_TOKEN",
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host=f"{{< influxdb/host >}}",
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database=f"DATABASE_NAME",
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flight_client_options={"middleware": (res,)})
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# Generate a trace ID for the query:
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# 1. Generate a random 8-byte value as bytes.
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# 2. Encode the value as hexadecimal.
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random_bytes = secrets.token_bytes(8)
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trace_id = random_bytes.hex()
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res.createTrace(trace_id)
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sql = "SELECT * FROM home WHERE time >= now() - INTERVAL '30 days'"
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client.query(sql)
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client.close()
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assert trace_id in res.traces[0], "Expect trace ID in DoGet response."
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use_middleware_trace_header()
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```
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{{% /code-placeholders %}}
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<!---- END PYTHON WITH MIDDLEWARE ---->
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{{% /tab-content %}}
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{{< /tabs-wrapper >}}
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Replace the following:
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- {{% code-placeholder-key %}}`DATABASE_NAME`{{% /code-placeholder-key %}}: your {{% product-name %}} database
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- {{% code-placeholder-key %}}`DATABASE_TOKEN`{{% /code-placeholder-key %}}: a [database token](/influxdb/cloud-dedicated/admin/tokens/) with sufficient permissions to the specified database
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{{% note %}}
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Store or log your query trace ID to ensure you can provide it to InfluxDB Support for troubleshooting.
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{{% /note %}}
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After you run your query with tracing enabled, do the following:
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- Remove the tracing header from subsequent runs of the query (to [avoid unnecessary tracing](#avoid-unnecessary-tracing)).
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- Provide the trace ID in a request to InfluxDB Support.
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## Retrieve query information
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In addition to the SQL standard `information_schema`, {{% product-name %}} contains _system_ tables that provide access to
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InfluxDB-specific information.
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The information in each system table is scoped to the namespace you're querying;
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you can only retrieve system information for that particular instance.
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To get information about queries you've run on the current instance, use SQL to query the [`system.queries` table](/influxdb/cloud-dedicated/reference/internals/system-tables/#systemqueries-measurement), which contains information from the querier instance currently handling queries.
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If you [enabled trace logging for the query](#enable-trace-logging-for-a-query), the `trace-id` appears in the `system.queries.trace_id` column for the query.
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The `system.queries` table is an InfluxDB v3 **debug feature**.
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To enable the feature and query `system.queries`, include an `"iox-debug"` header set to `"true"` and use SQL to query the table.
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The following sample code shows how to use the Python client library to do the following:
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1. Enable tracing for a query.
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2. Retrieve the trace ID record from `system.queries`.
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|
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<!-- Import for tests and hide from users.
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```python
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import os
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```
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-->
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|
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{{% code-placeholders "DATABASE_(NAME|TOKEN)|APP_REQUEST_ID" %}}
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<!--pytest-codeblocks:cont-->
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```python
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from influxdb_client_3 import InfluxDBClient3
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import secrets
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import pandas
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def get_query_information():
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print('# Get query information')
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client = InfluxDBClient3(token = f"DATABASE_TOKEN",
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host = f"{{< influxdb/host >}}",
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database = f"DATABASE_NAME")
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random_bytes = secrets.token_bytes(16)
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trace_id = random_bytes.hex()
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trace_value = (f"{trace_id}:1112223334445:0:1").encode('utf-8')
|
||||
sql = "SELECT * FROM home WHERE time >= now() - INTERVAL '30 days'"
|
||||
|
||||
try:
|
||||
client.query(sql, headers=[(b'influx-trace-id', trace_value)])
|
||||
client.close()
|
||||
except Exception as e:
|
||||
print("Query error: ", e)
|
||||
|
||||
client = InfluxDBClient3(token = f"DATABASE_TOKEN",
|
||||
host = f"{{< influxdb/host >}}",
|
||||
database = f"DATABASE_NAME")
|
||||
|
||||
import time
|
||||
df = pandas.DataFrame()
|
||||
|
||||
for i in range(0, 5):
|
||||
time.sleep(1)
|
||||
# Use SQL
|
||||
# To query the system.queries table for your trace ID, pass the following:
|
||||
# - the iox-debug: true request header
|
||||
# - an SQL query for the trace_id column
|
||||
reader = client.query(f'''SELECT compute_duration, query_type, query_text,
|
||||
success, trace_id
|
||||
FROM system.queries
|
||||
WHERE issue_time >= now() - INTERVAL '1 day'
|
||||
AND trace_id = '{trace_id}'
|
||||
ORDER BY issue_time DESC
|
||||
''',
|
||||
headers=[(b"iox-debug", b"true")],
|
||||
mode="reader")
|
||||
|
||||
df = reader.read_all().to_pandas()
|
||||
if df.shape[0]:
|
||||
break
|
||||
|
||||
assert df.shape == (1, 5), f"Expect a row for the query trace ID."
|
||||
print(df)
|
||||
|
||||
get_query_information()
|
||||
```
|
||||
{{% /code-placeholders %}}
|
||||
|
||||
The output is similar to the following:
|
||||
|
||||
```text
|
||||
compute_duration query_type query_text success trace_id
|
||||
0 days sql SELECT compute_duration, quer... True 67338...
|
||||
```
|
||||
|
|
@ -24,6 +24,7 @@ Learn how to handle responses and troubleshoot errors encountered when querying
|
|||
- [Internal Error: Received RST_STREAM](#internal-error-received-rst_stream)
|
||||
- [Internal Error: stream terminated by RST_STREAM with NO_ERROR](#internal-error-stream-terminated-by-rst_stream-with-no_error)
|
||||
- [Invalid Argument: Invalid ticket](#invalid-argument-invalid-ticket)
|
||||
- [Timeout: Deadline exceeded](#timeout-deadline-exceeded)
|
||||
- [Unauthenticated: Unauthenticated](#unauthenticated-unauthenticated)
|
||||
- [Unauthorized: Permission denied](#unauthorized-permission-denied)
|
||||
- [FlightUnavailableError: Could not get default pem root certs](#flightunavailableerror-could-not-get-default-pem-root-certs)
|
||||
|
|
@ -80,7 +81,8 @@ SELECT co, delete, hum, room, temp, time
|
|||
|
||||
The Python client library outputs the following schema representation:
|
||||
|
||||
```py
|
||||
<!--pytest.mark.skip-->
|
||||
```python
|
||||
Schema:
|
||||
co: int64
|
||||
-- field metadata --
|
||||
|
|
@ -175,7 +177,7 @@ _For a list of gRPC codes that servers and clients may return, see [Status codes
|
|||
|
||||
**Example**:
|
||||
|
||||
```sh
|
||||
```structuredtext
|
||||
Flight returned internal error, with message: Received RST_STREAM with error code 2. gRPC client debug context: UNKNOWN:Error received from peer ipv4:34.196.233.7:443 {grpc_message:"Received RST_STREAM with error code 2"}
|
||||
```
|
||||
|
||||
|
|
@ -192,11 +194,12 @@ Flight returned internal error, with message: Received RST_STREAM with error cod
|
|||
|
||||
**Example**:
|
||||
|
||||
<!--pytest.mark.skip-->
|
||||
```sh
|
||||
pyarrow._flight.FlightInternalError: Flight returned internal error, with message: stream terminated by RST_STREAM with error code: NO_ERROR. gRPC client debug context: UNKNOWN:Error received from peer ipv4:3.123.149.45:443 {created_time:"2023-07-26T14:12:44.992317+02:00", grpc_status:13, grpc_message:"stream terminated by RST_STREAM with error code: NO_ERROR"}. Client context: OK
|
||||
```
|
||||
|
||||
**Potential Reasons**:
|
||||
**Potential reasons**:
|
||||
|
||||
- The server terminated the stream, but there wasn't any specific error associated with it.
|
||||
- Possible network disruption, even if it's temporary.
|
||||
|
|
@ -208,21 +211,35 @@ pyarrow._flight.FlightInternalError: Flight returned internal error, with messag
|
|||
|
||||
**Example**:
|
||||
|
||||
<!--pytest.mark.skip-->
|
||||
```sh
|
||||
pyarrow.lib.ArrowInvalid: Flight returned invalid argument error, with message: Invalid ticket. Error: Invalid ticket. gRPC client debug context: UNKNOWN:Error received from peer ipv4:54.158.68.83:443 {created_time:"2023-08-31T17:56:42.909129-05:00", grpc_status:3, grpc_message:"Invalid ticket. Error: Invalid ticket"}. Client context: IOError: Server never sent a data message. Detail: Internal
|
||||
```
|
||||
|
||||
**Potential Reasons**:
|
||||
**Potential reasons**:
|
||||
|
||||
- The request is missing the database name or some other required metadata value.
|
||||
- The request contains bad query syntax.
|
||||
|
||||
<!-- END -->
|
||||
|
||||
#### Timeout: Deadline exceeded
|
||||
|
||||
<!--pytest.mark.skip-->
|
||||
```sh
|
||||
pyarrow._flight.FlightTimedOutError: Flight returned timeout error, with message: Deadline Exceeded. gRPC client debug context: UNKNOWN:Deadline Exceeded {grpc_status:4, created_time:"2023-09-27T15:30:58.540385-05:00"}. Client context: IOError: Server never sent a data message. Detail: Internal
|
||||
```
|
||||
|
||||
**Potential reasons**:
|
||||
|
||||
- The server's response time exceeded the number of seconds allowed by the client.
|
||||
See how to specify `timeout` in [FlightCallOptions](https://arrow.apache.org/docs/python/generated/pyarrow.flight.FlightCallOptions.html#pyarrow.flight.FlightCallOptions).
|
||||
|
||||
#### Unauthenticated: Unauthenticated
|
||||
|
||||
**Example**:
|
||||
|
||||
<!--pytest.mark.skip-->
|
||||
```sh
|
||||
Flight returned unauthenticated error, with message: unauthenticated. gRPC client debug context: UNKNOWN:Error received from peer ipv4:34.196.233.7:443 {grpc_message:"unauthenticated", grpc_status:16, created_time:"2023-08-28T15:38:33.380633-05:00"}. Client context: IOError: Server never sent a data message. Detail: Internal
|
||||
```
|
||||
|
|
@ -238,6 +255,7 @@ Flight returned unauthenticated error, with message: unauthenticated. gRPC clien
|
|||
|
||||
**Example**:
|
||||
|
||||
<!--pytest.mark.skip-->
|
||||
```sh
|
||||
pyarrow._flight.FlightUnauthorizedError: Flight returned unauthorized error, with message: Permission denied. gRPC client debug context: UNKNOWN:Error received from peer ipv4:54.158.68.83:443 {grpc_message:"Permission denied", grpc_status:7, created_time:"2023-08-31T17:51:08.271009-05:00"}. Client context: IOError: Server never sent a data message. Detail: Internal
|
||||
```
|
||||
|
|
@ -254,6 +272,7 @@ pyarrow._flight.FlightUnauthorizedError: Flight returned unauthorized error, wit
|
|||
|
||||
If unable to locate a root certificate for _gRPC+TLS_, the Flight client returns errors similar to the following:
|
||||
|
||||
<!--pytest.mark.skip-->
|
||||
```sh
|
||||
UNKNOWN:Failed to load file... filename:"/usr/share/grpc/roots.pem",
|
||||
children:[UNKNOWN:No such file or directory
|
||||
|
|
|
|||
|
|
@ -16,34 +16,68 @@ related:
|
|||
InfluxDB system measurements contain time series data used by and generated from the
|
||||
InfluxDB internal monitoring system.
|
||||
|
||||
Each InfluxDB Cloud Dedicated namespace includes the following system measurements:
|
||||
Each {{% product-name %}} namespace includes the following system measurements:
|
||||
|
||||
- [queries](#_queries-system-measurement)
|
||||
<!-- TOC -->
|
||||
|
||||
## queries system measurement
|
||||
- [system.queries measurement](#systemqueries-measurement)
|
||||
- [system.queries schema](#systemqueries-schema)
|
||||
|
||||
## system.queries measurement
|
||||
|
||||
The `system.queries` measurement stores log entries for queries executed for the provided namespace (database) on the node that is currently handling queries.
|
||||
|
||||
The following example shows how to list queries recorded in the `system.queries` measurement:
|
||||
```python
|
||||
from influxdb_client_3 import InfluxDBClient3
|
||||
client = InfluxDBClient3(token = DATABASE_TOKEN,
|
||||
host = HOSTNAME,
|
||||
org = '',
|
||||
database=DATABASE_NAME)
|
||||
client.query('select * from home')
|
||||
reader = client.query('''
|
||||
SELECT *
|
||||
FROM system.queries
|
||||
WHERE issue_time >= now() - INTERVAL '1 day'
|
||||
AND query_text LIKE '%select * from home%'
|
||||
''',
|
||||
language='sql',
|
||||
headers=[(b"iox-debug", b"true")],
|
||||
mode="reader")
|
||||
print("# system.queries schema\n")
|
||||
print(reader.schema)
|
||||
```
|
||||
|
||||
```sql
|
||||
SELECT issue_time, query_type, query_text, success FROM system.queries;
|
||||
<!--pytest-codeblocks:expected-output-->
|
||||
|
||||
`system.queries` has the following schema:
|
||||
|
||||
```python
|
||||
# system.queries schema
|
||||
|
||||
issue_time: timestamp[ns] not null
|
||||
query_type: string not null
|
||||
query_text: string not null
|
||||
completed_duration: duration[ns]
|
||||
success: bool not null
|
||||
trace_id: string
|
||||
```
|
||||
|
||||
_When listing measurements (tables) available within a namespace, some clients and query tools may include the `queries` table in the list of namespace tables._
|
||||
|
||||
`system.queries` reflects a process-local, in-memory, namespace-scoped query log.
|
||||
The query log isn't shared across instances within the same deployment.
|
||||
While this table may be useful for debugging and monitoring queries, keep the following in mind:
|
||||
|
||||
- Records stored in `system.queries` are volatile.
|
||||
- Records are lost on pod restarts.
|
||||
- Queries for one namespace can evict records from another namespace.
|
||||
- Data reflects the state of a specific pod answering queries for the namespace.
|
||||
- Data reflects the state of a specific pod answering queries for the namespace----the log view is scoped to the requesting namespace and queries aren't leaked across namespaces.
|
||||
- A query for records in `system.queries` can return different results depending on the pod the request was routed to.
|
||||
|
||||
**Data retention:** System data can be transient and is deleted on pod restarts.
|
||||
The log size per instance is limited and the log view is scoped to the requesting namespace.
|
||||
|
||||
### queries measurement schema
|
||||
### system.queries schema
|
||||
|
||||
- **system.queries** _(measurement)_
|
||||
- **fields**:
|
||||
|
|
|
|||
|
|
@ -26,6 +26,7 @@ related:
|
|||
- /influxdb/cloud-serverless/query-data/sql/
|
||||
- /influxdb/cloud-serverless/reference/influxql/
|
||||
- /influxdb/cloud-serverless/reference/sql/
|
||||
- /influxdb/cloud-serverless/query-data/execute-queries/troubleshoot/
|
||||
|
||||
list_code_example: |
|
||||
```py
|
||||
|
|
@ -33,7 +34,7 @@ list_code_example: |
|
|||
|
||||
# Instantiate an InfluxDB client
|
||||
client = InfluxDBClient3(
|
||||
host='cloud2.influxdata.com',
|
||||
host='{{< influxdb/host >}}',
|
||||
token='DATABASE_TOKEN',
|
||||
database='DATABASE_NAME'
|
||||
)
|
||||
|
|
@ -306,7 +307,7 @@ and specify the following arguments:
|
|||
|
||||
#### Example {#execute-query-example}
|
||||
|
||||
The following examples show how to use SQL or InfluxQL to select all fields in a measurement, and then output the results formatted as a Markdown table.
|
||||
The following example shows how to use SQL or InfluxQL to select all fields in a measurement, and then use PyArrow functions to extract metadata and aggregate data.
|
||||
|
||||
{{% code-tabs-wrapper %}}
|
||||
{{% code-tabs %}}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,106 @@
|
|||
---
|
||||
title: Optimize queries
|
||||
description: >
|
||||
Optimize your SQL and InfluxQL queries to improve performance and reduce their memory and compute (CPU) requirements.
|
||||
weight: 401
|
||||
menu:
|
||||
influxdb_cloud_serverless:
|
||||
name: Optimize queries
|
||||
parent: Execute queries
|
||||
influxdb/cloud-serverless/tags: [query, sql, influxql]
|
||||
related:
|
||||
- /influxdb/cloud-serverless/query-data/sql/
|
||||
- /influxdb/cloud-serverless/query-data/influxql/
|
||||
- /influxdb/cloud-serverless/query-data/execute-queries/troubleshoot/
|
||||
- /influxdb/cloud-serverless/reference/client-libraries/v3/
|
||||
---
|
||||
|
||||
## Troubleshoot query performance
|
||||
|
||||
Use the following tools to help you identify performance bottlenecks and troubleshoot problems in queries:
|
||||
|
||||
<!-- TOC -->
|
||||
|
||||
- [Troubleshoot query performance](#troubleshoot-query-performance)
|
||||
- [EXPLAIN and ANALYZE](#explain-and-analyze)
|
||||
- [Enable trace logging](#enable-trace-logging)
|
||||
|
||||
<!-- /TOC -->
|
||||
|
||||
### EXPLAIN and ANALYZE
|
||||
|
||||
To view the query engine's execution plan and metrics for an SQL query, prepend [`EXPLAIN`](/influxdb/cloud-serverless/reference/sql/explain/) or [`EXPLAIN ANALYZE`](/influxdb/cloud-serverless/reference/sql/explain/#explain-analyze) to the query.
|
||||
The report can reveal query bottlenecks such as a large number of table scans or parquet files, and can help triage the question, "Is the query slow due to the amount of work required or due to a problem with the schema, compactor, etc.?"
|
||||
|
||||
The following example shows how to use the InfluxDB v3 Python client library and pandas to view `EXPLAIN` and `EXPLAIN ANALYZE` results for a query:
|
||||
|
||||
<!-- Import for tests and hide from users.
|
||||
```python
|
||||
import os
|
||||
```
|
||||
-->
|
||||
<!--pytest-codeblocks:cont-->
|
||||
{{% code-placeholders "BUCKET_NAME|API_TOKEN|APP_REQUEST_ID" %}}
|
||||
```python
|
||||
from influxdb_client_3 import InfluxDBClient3
|
||||
import pandas as pd
|
||||
import tabulate # Required for pandas.to_markdown()
|
||||
|
||||
def explain_and_analyze():
|
||||
print('Use SQL EXPLAIN and ANALYZE to view query plan information.')
|
||||
|
||||
# Instantiate an InfluxDB client.
|
||||
client = InfluxDBClient3(token = f"API_TOKEN",
|
||||
host = f"{{< influxdb/host >}}",
|
||||
database = f"BUCKET_NAME")
|
||||
|
||||
sql_explain = '''EXPLAIN SELECT *
|
||||
FROM home
|
||||
WHERE time >= now() - INTERVAL '90 days'
|
||||
ORDER BY time'''
|
||||
|
||||
table = client.query(sql_explain)
|
||||
df = table.to_pandas()
|
||||
|
||||
sql_explain_analyze = '''EXPLAIN ANALYZE SELECT *
|
||||
FROM home
|
||||
WHERE time >= now() - INTERVAL '90 days'
|
||||
ORDER BY time'''
|
||||
|
||||
table = client.query(sql_explain_analyze)
|
||||
|
||||
# Combine the Dataframes and output the plan information.
|
||||
df = pd.concat([df, table.to_pandas()])
|
||||
|
||||
assert df.shape == (3, 2) and df.columns.to_list() == ['plan_type', 'plan']
|
||||
print(df[['plan_type', 'plan']].to_markdown(index=False))
|
||||
|
||||
client.close()
|
||||
|
||||
explain_and_analyze()
|
||||
```
|
||||
{{% /code-placeholders %}}
|
||||
|
||||
Replace the following:
|
||||
|
||||
- {{% code-placeholder-key %}}`BUCKET_NAME`{{% /code-placeholder-key %}}: your {{% product-name %}} database
|
||||
- {{% code-placeholder-key %}}`API_TOKEN`{{% /code-placeholder-key %}}: a [database token](/influxdb/cloud-serverless/admin/tokens/) with sufficient permissions to the specified database
|
||||
|
||||
The output is similar to the following:
|
||||
|
||||
```markdown
|
||||
| plan_type | plan |
|
||||
|:------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| logical_plan | Sort: home.time ASC NULLS LAST |
|
||||
| | TableScan: home projection=[co, hum, room, sensor, temp, time], full_filters=[home.time >= TimestampNanosecond(1688491380936276013, None)] |
|
||||
| physical_plan | SortExec: expr=[time@5 ASC NULLS LAST] |
|
||||
| | EmptyExec: produce_one_row=false |
|
||||
| Plan with Metrics | SortExec: expr=[time@5 ASC NULLS LAST], metrics=[output_rows=0, elapsed_compute=1ns, spill_count=0, spilled_bytes=0] |
|
||||
| | EmptyExec: produce_one_row=false, metrics=[]
|
||||
```
|
||||
|
||||
### Enable trace logging
|
||||
|
||||
Customers with an {{% product-name %}} [annual or support contract](https://www.influxdata.com/influxdb-cloud-pricing/) can [contact InfluxData Support](https://support.influxdata.com/) to enable tracing and request help troubleshooting your query.
|
||||
With tracing enabled, InfluxDB Support can trace system processes and analyze log information for a query instance.
|
||||
The tracing system follows the [OpenTelemetry traces](https://opentelemetry.io/docs/concepts/signals/traces/) model for providing observability into a request.
|
||||
|
|
@ -197,7 +197,7 @@ Flight returned internal error, with message: Received RST_STREAM with error cod
|
|||
pyarrow._flight.FlightInternalError: Flight returned internal error, with message: stream terminated by RST_STREAM with error code: NO_ERROR. gRPC client debug context: UNKNOWN:Error received from peer ipv4:3.123.149.45:443 {created_time:"2023-07-26T14:12:44.992317+02:00", grpc_status:13, grpc_message:"stream terminated by RST_STREAM with error code: NO_ERROR"}. Client context: OK
|
||||
```
|
||||
|
||||
**Potential Reasons**:
|
||||
**Potential reasons**:
|
||||
|
||||
- The server terminated the stream, but there wasn't any specific error associated with it.
|
||||
- Possible network disruption, even if it's temporary.
|
||||
|
|
@ -213,7 +213,7 @@ pyarrow._flight.FlightInternalError: Flight returned internal error, with messag
|
|||
ArrowInvalid: Flight returned invalid argument error, with message: bucket "otel5" not found. gRPC client debug context: UNKNOWN:Error received from peer ipv4:3.123.149.45:443 {grpc_message:"bucket \"otel5\" not found", grpc_status:3, created_time:"2023-08-09T16:37:30.093946+01:00"}. Client context: IOError: Server never sent a data message. Detail: Internal
|
||||
```
|
||||
|
||||
**Potential Reasons**:
|
||||
**Potential reasons**:
|
||||
|
||||
- The specified bucket doesn't exist.
|
||||
|
||||
|
|
@ -227,7 +227,7 @@ ArrowInvalid: Flight returned invalid argument error, with message: bucket "otel
|
|||
pyarrow.lib.ArrowInvalid: Flight returned invalid argument error, with message: Invalid ticket. Error: Invalid ticket. gRPC client debug context: UNKNOWN:Error received from peer ipv4:54.158.68.83:443 {created_time:"2023-08-31T17:56:42.909129-05:00", grpc_status:3, grpc_message:"Invalid ticket. Error: Invalid ticket"}. Client context: IOError: Server never sent a data message. Detail: Internal
|
||||
```
|
||||
|
||||
**Potential Reasons**:
|
||||
**Potential reasons**:
|
||||
|
||||
- The request is missing the bucket name or some other required metadata value.
|
||||
- The request contains bad query syntax.
|
||||
|
|
|
|||
|
|
@ -0,0 +1,115 @@
|
|||
---
|
||||
title: Optimize queries
|
||||
description: >
|
||||
Optimize your SQL and InfluxQL queries to improve performance and reduce their memory and compute (CPU) requirements.
|
||||
weight: 401
|
||||
menu:
|
||||
influxdb_clustered:
|
||||
name: Optimize queries
|
||||
parent: Execute queries
|
||||
influxdb/clustered/tags: [query, sql, influxql]
|
||||
related:
|
||||
- /influxdb/clustered/query-data/sql/
|
||||
- /influxdb/clustered/query-data/influxql/
|
||||
- /influxdb/clustered/query-data/execute-queries/troubleshoot/
|
||||
- /influxdb/clustered/reference/client-libraries/v3/
|
||||
---
|
||||
|
||||
Use the following tools to help you identify performance bottlenecks and troubleshoot problems in queries:
|
||||
|
||||
<!-- TOC -->
|
||||
|
||||
- [EXPLAIN and ANALYZE](#explain-and-analyze)
|
||||
|
||||
<!-- /TOC -->
|
||||
|
||||
### EXPLAIN and ANALYZE
|
||||
|
||||
To view the query engine's execution plan and metrics for an SQL query, prepend [`EXPLAIN`](/influxdb/clustered/reference/sql/explain/) or [`EXPLAIN ANALYZE`](/influxdb/clustered/reference/sql/explain/#explain-analyze) to the query.
|
||||
The report can reveal query bottlenecks such as a large number of table scans or parquet files, and can help triage the question, "Is the query slow due to the amount of work required or due to a problem with the schema, compactor, etc.?"
|
||||
|
||||
The following example shows how to use the InfluxDB v3 Python client library and pandas to view `EXPLAIN` and `EXPLAIN ANALYZE` results for a query:
|
||||
|
||||
<!-- Import for tests and hide from users.
|
||||
```python
|
||||
import os
|
||||
```
|
||||
-->
|
||||
|
||||
{{% code-placeholders "DATABASE_(NAME|TOKEN)" %}}
|
||||
<!--pytest-codeblocks:cont-->
|
||||
|
||||
```python
|
||||
from influxdb_client_3 import InfluxDBClient3
|
||||
import pandas as pd
|
||||
import tabulate # Required for pandas.to_markdown()
|
||||
|
||||
# Instantiate an InfluxDB client.
|
||||
client = InfluxDBClient3(token = f"DATABASE_TOKEN",
|
||||
host = f"{{< influxdb/host >}}",
|
||||
database = f"DATABASE_NAME")
|
||||
|
||||
sql_explain = '''EXPLAIN
|
||||
SELECT temp
|
||||
FROM home
|
||||
WHERE time >= now() - INTERVAL '90 days'
|
||||
AND room = 'Kitchen'
|
||||
ORDER BY time'''
|
||||
|
||||
table = client.query(sql_explain)
|
||||
df = table.to_pandas()
|
||||
print(df.to_markdown(index=False))
|
||||
|
||||
assert df.shape == (2, 2), f'Expect {df.shape} to have 2 columns, 2 rows'
|
||||
assert 'physical_plan' in df.plan_type.values, "Expect physical_plan"
|
||||
assert 'logical_plan' in df.plan_type.values, "Expect logical_plan"
|
||||
```
|
||||
|
||||
{{< expand-wrapper >}}
|
||||
{{% expand "View EXPLAIN example results" %}}
|
||||
| plan_type | plan |
|
||||
|:--------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| logical_plan | Projection: home.temp |
|
||||
| | Sort: home.time ASC NULLS LAST |
|
||||
| | Projection: home.temp, home.time |
|
||||
| | TableScan: home projection=[room, temp, time], full_filters=[home.time >= TimestampNanosecond(1688676582918581320, None), home.room = Dictionary(Int32, Utf8("Kitchen"))] |
|
||||
| physical_plan | ProjectionExec: expr=[temp@0 as temp] |
|
||||
| | SortExec: expr=[time@1 ASC NULLS LAST] |
|
||||
| | EmptyExec: produce_one_row=false |
|
||||
{{% /expand %}}
|
||||
{{< /expand-wrapper >}}
|
||||
|
||||
<!--pytest-codeblocks:cont-->
|
||||
|
||||
```python
|
||||
sql_explain_analyze = '''EXPLAIN ANALYZE
|
||||
SELECT *
|
||||
FROM home
|
||||
WHERE time >= now() - INTERVAL '90 days'
|
||||
ORDER BY time'''
|
||||
|
||||
table = client.query(sql_explain_analyze)
|
||||
df = table.to_pandas()
|
||||
print(df.to_markdown(index=False))
|
||||
|
||||
assert df.shape == (1,2)
|
||||
assert 'Plan with Metrics' in df.plan_type.values, "Expect plan metrics"
|
||||
|
||||
client.close()
|
||||
```
|
||||
{{% /code-placeholders %}}
|
||||
|
||||
Replace the following:
|
||||
|
||||
- {{% code-placeholder-key %}}`DATABASE_NAME`{{% /code-placeholder-key %}}: your {{% product-name %}} database
|
||||
- {{% code-placeholder-key %}}`DATABASE_TOKEN`{{% /code-placeholder-key %}}: a [database token](/influxdb/cloud-dedicated/admin/tokens/) with sufficient permissions to the specified database
|
||||
|
||||
{{< expand-wrapper >}}
|
||||
{{% expand "View EXPLAIN ANALYZE example results" %}}
|
||||
| plan_type | plan |
|
||||
|:------------------|:-----------------------------------------------------------------------------------------------------------------------|
|
||||
| Plan with Metrics | ProjectionExec: expr=[temp@0 as temp], metrics=[output_rows=0, elapsed_compute=1ns] |
|
||||
| | SortExec: expr=[time@1 ASC NULLS LAST], metrics=[output_rows=0, elapsed_compute=1ns, spill_count=0, spilled_bytes=0] |
|
||||
| | EmptyExec: produce_one_row=false, metrics=[]
|
||||
{{% /expand %}}
|
||||
{{< /expand-wrapper >}}
|
||||
|
|
@ -197,7 +197,7 @@ Flight returned internal error, with message: Received RST_STREAM with error cod
|
|||
pyarrow._flight.FlightInternalError: Flight returned internal error, with message: stream terminated by RST_STREAM with error code: NO_ERROR. gRPC client debug context: UNKNOWN:Error received from peer ipv4:3.123.149.45:443 {created_time:"2023-07-26T14:12:44.992317+02:00", grpc_status:13, grpc_message:"stream terminated by RST_STREAM with error code: NO_ERROR"}. Client context: OK
|
||||
```
|
||||
|
||||
**Potential Reasons**:
|
||||
**Potential reasons**:
|
||||
|
||||
- The server terminated the stream, but there wasn't any specific error associated with it.
|
||||
- Possible network disruption, even if it's temporary.
|
||||
|
|
@ -213,7 +213,7 @@ pyarrow._flight.FlightInternalError: Flight returned internal error, with messag
|
|||
pyarrow.lib.ArrowInvalid: Flight returned invalid argument error, with message: Invalid ticket. Error: Invalid ticket. gRPC client debug context: UNKNOWN:Error received from peer ipv4:54.158.68.83:443 {created_time:"2023-08-31T17:56:42.909129-05:00", grpc_status:3, grpc_message:"Invalid ticket. Error: Invalid ticket"}. Client context: IOError: Server never sent a data message. Detail: Internal
|
||||
```
|
||||
|
||||
**Potential Reasons**:
|
||||
**Potential reasons**:
|
||||
|
||||
- The request is missing the database name or some other required metadata value.
|
||||
- The request contains bad query syntax.
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
## Code sample dependencies
|
||||
influxdb3-python
|
||||
# Temporary fork for passing headers in query options.
|
||||
influxdb3-python @ git+https://github.com/jstirnaman/influxdb3-python@4abd41c710e79f85333ba81258b10daff54d05b0
|
||||
pandas
|
||||
## Tabulate for printing pandas DataFrames.
|
||||
tabulate
|
||||
|
|
|
|||
Loading…
Reference in New Issue