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As data grows exponentially, organizations are continuously seeking more efficient and powerful tools to manage and analyze their data. The Query tab, which utilizes the Axiom Processing Language (APL), is one such service that offers fast, scalable, and interactive data exploration capabilities. If you are an SQL user looking to migrate to APL, this guide will provide a gentle introduction to help you make the transition smoothly.
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As data grows exponentially, organizations are continuously seeking more efficient and powerful tools to manage and analyze their data. The Explore tab, which utilizes the Axiom Processing Language (APL), is one such service that offers fast, scalable, and interactive data exploration capabilities. If you are an SQL user looking to migrate to APL, this guide will provide a gentle introduction to help you make the transition smoothly.
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**This tutorial will guide you through migrating SQL to APL, helping you understand key differences and providing you with query examples.**
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## Introduction to Axiom Processing Language (APL)
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Axiom Processing Language (APL) is the language used by the Query tab, a fast and highly scalable data exploration service. APL is optimized for real-time and historical data analytics, making it a suitable choice for various data analysis tasks.
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Axiom Processing Language (APL) is the language used by the Explore tab, a fast and highly scalable data exploration service. APL is optimized for real-time and historical data analytics, making it a suitable choice for various data analysis tasks.
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**Tabular operators**: In APL, there are several tabular operators that help you manipulate and filter data, similar to SQL’s SELECT, FROM, WHERE, GROUP BY, and ORDER BY clauses. Some of the commonly used tabular operators are:
Splunk and Axiom are powerful tools for log analysis and data exploration. The data explorer interface uses Axiom Processing Language (APL). There are some differences between the query languages for Splunk and Axiom. When transitioning from Splunk to APL, you will need to understand how to convert your Splunk SPL queries into APL.
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Splunk and Axiom data Explorer are powerful tools for log analysis and data exploration. The data explorer interface uses Axiom Processing Language (APL). There are some differeces between the query languages for Splunk and Axiom. When transitioning from Splunk to APL, you will need to understand how to convert your Splunk SPL queries into APL.
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**This guide provides a high-level mapping from Splunk to APL.**
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Whether you’re just starting your transition or you’re in the thick of it, this guide can serve as a helpful roadmap to assist you in your journey from Splunk to Axiom Processing Language.
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Dive into the Axiom Processing Language, start converting your Splunk queries to APL, and explore the rich capabilities of the Query tab. Embrace the learning curve, and remember, every complex query you master is another step forward in your data analytics journey.
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Dive into the Axiom Processing Language, start converting your Splunk queries to APL, and explore the rich capabilities of the Explore tab. Embrace the learning curve, and remember, every complex query you master is another step forward in your data analytics journey.
Copy file name to clipboardexpand all lines: apl/introduction.mdx
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## Getting Started
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Go to the Query tab and click one of your datasets to get started. The APL editor has full auto-completion so you can poke around or you can get a better understanding of all the features by using the reference menu to the left of this page.
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Go to the Explore tab and click one of your datasets to get started. The APL editor has full auto-completion so you can poke around or you can get a better understanding of all the features by using the reference menu to the left of this page.
In this tutorial, you’ll explore how to use APL in Axiom’s Query tab to run queries using Tabular Operators, Scalar Functions, and Aggregation Functions.
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In this tutorial, you’ll explore how to use APL in Axiom’s Explore tab to run queries using Tabular Operators, Scalar Functions, and Aggregation Functions.
Copy file name to clipboardexpand all lines: dashboard-elements/create.mdx
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1.[Create a dashboard](/dashboards/create) or open an existing dashboard.
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1. Click <imgsrc="/doc-assets/icons/plus.svg"className="inline-icon"alt="Add chart" /> **Add chart** in the top right corner.
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1. Choose the dashboard element from the list.
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1. For charts, select one of the following:
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- Click **Simple Query Builder** to create your chart using a [visual query builder](#create-chart-using-visual-query-builder).
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- Click **Advanced Query Language** to create your chart using the Axiom Processing Language (APL). Create a chart in the same way you create a chart in the APL query builder of the [Query tab](/query-data/explore#create-a-query-using-apl).
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1. Choose the dashboard element from the list. For more information on each dashboard element, choose one of the following:
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-[Filter bar](/query-data/filters)
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-[Log stream](/dashboard-elements/log-stream)
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-[Scatter plot](/dashboard-elements/scatter-plot)
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-[Statistic](/dashboard-elements/statistic)
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-[Table](/dashboard-elements/table)
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-[Time series](/dashboard-elements/time-series)
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1. Choose one of the following:
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- Click **Simple Query Builder** to create your chart using a visual query builder. Create a chart in the same way you create a chart in the [Datasets tab](/query-data/datasets).
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- Click **Advanced Query Language** to create your chart using the Axiom Processing Language (APL). Create a chart in the same way you create a chart in the APL query builder of the [Explore tab](/query-data/explore#create-a-query-using-apl).
This component is a visual query builder that eases the process of building visualizations and segments of your data.
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This guide walks you through the individual sections of the query builder.
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### Time range
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Every query has a start and end time and the time range component allows quick selection of common time ranges as well as the ability to input specific start and end timestamps:
- Use the **Quick Range** items to quickly select popular ranges
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- Use the **Custom Start/End Date** inputs to select specific times
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- Use the **Resolution** items to choose between various time bucket resolutions
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### Against
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When a time series visualization is selected, such as `count`, the **Against** menu is enabled and it’s possible to select a historical time to compare the results of your time range too.
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For example, to compare the last hour’s average response time to the same time yesterday, select `1 hr` in the time range menu, and then select `-1D` from the **Against** menu:
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<Framecaption="Time range against menu">
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<imgsrc="/doc-assets/shots/compare-against.png"alt="Time range against menu" />
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</Frame>
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The results look like this:
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<Framecaption="Time range against chart">
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<imgsrc="/doc-assets/shots/time-range-chart-1.png"alt="Time range against chart" />
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</Frame>
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The dotted line represents results from the base date, and the totals table includes the comparative totals.
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When you add `field` to the `group by` clause, the **time range against** values are attached to each `events`.
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<Framecaption="Time range against chart">
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<imgsrc="/doc-assets/shots/time-range-chart-2.png"alt="Time range against chart" />
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</Frame>
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### Visualizations
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Axiom provides powerful visualizations that display the output of running aggregate functions across your dataset. The Visualization menu allows you to add these visualizations and, where required, input their arguments:
You can select a visualization to add it to the query. If a visualization requires an argument (such as the field and/or other parameters), the menu allows you to select eligible fields and input those arguments. Press `Enter` to complete the addition:
Click Visualization in the query builder to edit it at any time.
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[Learn about supported visualizations](/query-data/visualizations)
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### Filters
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Use the filter menu to attach filter clauses to your search.
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Axiom supports AND/OR operators at the top-level as well as one level deep. This means you can create filters that would read as `status == 200 AND (method == get OR method == head) AND (user-agent contains Mozilla or user-agent contains Webkit)`.
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Filters are divided up by the field type they operate on, but some may apply to more than one field type.
Axiom creates the following two fields automatically for a new dataset:
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-`_time` is the timestamp of the event. If the data you ingest doesn’t have a `_time` field, Axiom assigns the time of the data ingest to the events.
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-`_sysTime` is the time when you ingested the data.
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In most cases, you can use `_time` and `_sysTime` interchangeably. The difference between them can be useful if you experience clock skews on your event-producing systems.
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### Group by (segmentation)
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When visualizing data, it can be useful to segment data into specific groups to more clearly understand how the data behaves.
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The Group By component enables you to add one or more fields to group events by:
By default, Axiom automatically chooses the best ordering for results. However, you can manually set the desired order through this menu.
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#### Limit
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By default, Axiom chooses a reasonable limit for the query that has been passed in. However, you can control that limit manually through this component.
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