Splunk’s Search Processing Language (SPL) offers a wide array of commands to help users analyze and visualize their data effectively. Among these powerful tools is the xyseries command, which, while not as commonly used as some other commands, can be incredibly useful for transforming data into a format suitable for creating multi-series visualizations. This article will explore the xyseries command, its uses, and how it can enhance your Splunk data presentations.
Using the xyseries Command
The xyseries command in Splunk can be a distributable streaming command or a transforming command. It is used to reshape data into a format suitable for creating charts with multiple series. This is particularly useful when you want to compare different metrics across various categories or time periods. It takes a set of field values and turns them into a table where each unique combination of specified fields becomes a column.
Benefits of the xyseries Command
- Simplified Data Transformation – xyseries offers a quick way to reshape your data into a format suitable for multi-series charts, saving you from complex SPL manipulations.
- Enhanced Visualization Capabilities – By transforming data into a multi-series format, it enables the creation of more complex and informative visualizations.
- Comparative Analysis – The command facilitates side-by-side comparison of different categories or metrics, making it easier to spot trends and patterns.
Proper Syntax
The basic syntax of the xyseries command is:
| xyseries [grouped=<bool>] <x-field> <y-name-field> <y-data-field(s)> [sep=<string>] [format=<string>]
Here’s a breakdown:
- Xyseries – Required and begins the command.
- [grouped=<bool>] – Set as true to treat multiple fields as a single unit or group on the y-axis.
- <x-field> – This will appear as your x-axis on the table.
- <y-name-field> – These will be the values that your y-axis uses.
- <y-data-field> – One or more fields you wish to chart, separated with commas.
- [sep=<string>] – Shows how you want to separate the values within the table.
- [format=<string>] – Lets you define how to handle cases where there are multiple entries for a x and y field. If this conflicts with [sep], this will take priority.
Example Use Cases
Example #1: Web Traffic Analysis
Suppose you want to compare web traffic across different pages of your website over time:
index=web_traffic
| stats count by page, date_hour
| xyseries date_hour page count
This query will create a table where each column represents a different page, and each row represents an hour, with the cell values showing the traffic count. This format is perfect for creating a multi-line chart of page traffic over time.
Example #2: Sales Performance by Product Category
If you’re analyzing sales data and want to compare different product categories:
index=sales
| stats sum(revenue) as total_revenue by category, quarter
| xyseries quarter category total_revenue
This will produce a table where each column is a product category, each row is a quarter, and the cell values show the total revenue. This structure is ideal for creating a stacked bar chart or line graph comparing category performance over time.
Using xyseries in Dashboards & Apps
The true potential emerges when integrated into specialized Splunk apps and dashboard solutions. Here are several practical applications where xyseries can enhance your Splunk deployment:
1. Network Performance Analysis
Build visualizations that compare bandwidth usage, latency, and packet loss across multiple network segments:
index=network sourcetype=cisco:asa
| stats avg(bandwidth_mbps) as bandwidth, avg(latency_ms) as latency by interface, _time span=5m
| xyseries _time interface bandwidth latency
2. Threat Intelligence Dashboards
Compare different types of security events across various network zones:
index=security sourcetype=firewall_
| eval severity=case(priority=1,"Critical",priority=2,"High",priority=3,"Medium",priority=4,"Low")
| stats count by severity, network_zone, _time span=1h
| xyseries _time network_zone severity count
3. User Behavior Analytics
Track multiple user activity metrics:
index=security sourcetype=windows_security
| stats count(eval(action="login")) as logins, count(eval(action="file_access")) as file_access, count(eval(action="privilege_change")) as priv_changes by user, _time span=4h
| xyseries _time user logins file_access priv_changes
4. Sales Analytics
Compare product performance across regions:
index=sales sourcetype=transactions
| stats sum(revenue) as total_revenue, sum(units_sold) as units by product_category, region, _time span=1d
| xyseries _time region product_category total_revenue
5. Marketing Campaign Analysis
Track multiple marketing metrics:
index=marketing sourcetype=campaign_metrics
| stats avg(conversion_rate) as conv_rate, avg(click_through_rate) as ctr, sum(impressions) as impressions by campaign_name, _time span=1h
| xyseries _time campaign_name conv_rate ctr impressions
Best Practices & Considerations
- Consolidate Values – Consider the number of unique values in your series field. Too many unique values can result in a very wide table that might be difficult to visualize effectively.
- Order of Operations – Properly ordering fields with the xyseries command is crucial. The last field becomes the values in the cells, the second-to-last field becomes the column headers, and any preceding fields become the row identifiers.
- Formatting – For time-based analyses, consider using appropriate time-based functions (like strftime) to format your time field before applying xyseries.
Conclusion
The xyseries command in Splunk is a valuable tool for transforming data into formats suitable for complex, multi-series visualizations. Whether you’re performing time-series analysis, comparing performance across different categories, or trying to identify patterns in multidimensional data, xyseries can provide the data structure you need for insightful visualizations. By incorporating xyseries into your Splunk toolkit, you can enhance your ability to create compelling, informative charts and graphs, leading to more effective data presentations and deeper insights from your Splunk data.
To access more Splunk searches, check out Atlas Search Library, which is part of the Atlas Platform. Specifically, Atlas Search Library offers a curated list of optimized searches. These searches empower Splunk users without requiring SPL knowledge. Furthermore, you can create, customize, and maintain your own search library. By doing so, you ensure your users get the most from using Splunk.
