The goal is to turn data into information, and information into insight.

目标是将数据转化为信息，并将信息转化为洞察力。

―Carly Fiorina

―卡莉·菲奥莉娜(Carly Fiorina)

关于基巴纳 (About Kibana)

Kibana is a piece of data visualization software that provides a browser-based interface for exploring Elasticsearch data and navigating the Elastic Stack — a collection of open-source products (Elasticsearch, Logstash, Beats, and others).

Kibana是一款数据可视化软件 ，它提供了一个基于浏览器的界面，用于浏览Elasticsearch数据并浏览Elastic Stack (一组开源产品(Elasticsearch，Logstash，Beats等)。

While Logstash and Bits deliver data to Elasticsearch, Kibana opens the window into the Elastic Stack, allowing you to track the health of your cluster, perform log and time-series analysis, detect anomalies in the data with unsupervised machine learning, discover relationships using graphs and, most importantly, extract insights from the Elasticsearch data with visualizations that can be combined together in a custom interactive dashboard.

当Logstash和Bits将数据传递到Elasticsearch时， Kibana 将打开一个窗口进入Elastic Stack ，使您可以跟踪集群的运行状况 ，执行日志和时间序列分析 ，通过无监督的机器学习来检测数据中的异常情况，使用图发现关系最重要的是，可以从Elasticsearch数据中提取具有可视化效果的见解，这些可视化效果可以在自定义的交互式仪表板中组合在一起。

Today I’d like to show you how to create a stunning dashboard and a tabular report based on the Elasticsearch data.

今天，我想向您展示如何基于Elasticsearch数据创建出色的仪表板和表格报告 。

Roll up your sleeves and let’s start!

卷起袖子，让我们开始吧！

从哪儿开始 (Where to start)

The Home page is the place where everything starts.

主页是一切开始的地方。

Here you can decide which actions to take next. The available functionality can be divided into two logical sections:

在这里您可以决定下一步要采取的行动。 可用功能可以分为两个逻辑部分：

• Visualizing and exploring the data. Here you can create a new dashboard, visualization or presentation, build a machine learning model, analyze relationships in your data using graphs, and more.

  可视化和探索数据。 在这里，您可以创建新的仪表板，可视化或演示文稿，构建机器学习模型，使用图形分析数据中的关系等。

• Managing the Elastic Stack: configure your spaces, analyze logs of an application, configure security settings, etc.

  管理 弹性堆栈 ：配置空间，分析应用程序日志，配置安全性设置等。

We’ll focus on the process of creating visualizations and adding them to the dashboard.

我们将专注于创建可视化并将其添加到仪表板的过程。

如何在Kibana中创建仪表板 (How to create a dashboard in Kibana)

Let me get you a feel for how easy it is to set up a rich dashboard and start reporting.

让我感觉一下，设置一个丰富的仪表板并开始报告是多么容易。

The first essential step to take is to import your data into Kibana. Multiple options for adding data are at your disposal — you can choose the one that works best for you:

第一步是将数据导入Kibana。 您可以使用多种选项来添加数据-您可以选择最适合自己的一种：

For demonstration purposes, I’ve selected the sample data.

为了演示，我选择了示例数据。

To design your first data visualizations and combine them into the dashboard, open the Visualize page. Here you can create, modify and view the existing visualizations.

要设计您的第一个数据可视化并将其组合到仪表板中，请打开“ 可视化”页面。 在这里您可以创建，修改和查看现有的可视化。

What will strike you at once is the abundance of visualization types you can choose from.

一下子吸引您的是您可以选择的大量可视化类型 。

After you’ve selected the one you need, choose an index pattern as a source so as to inform Kibana about your index. Let’s choose `kibana_sample_data_flights` and start creating a horizontal bar chart.

选择所需的索引后，请选择一种索引模式作为来源，以便将有关索引的信息告知Kibana。 让我们选择“ kibana_sample_data_flights”并开始创建水平条形图。

Now you can apply a metric aggregation for the Y-axis and a bucket aggregation for the X-axis. Here is a list of all available aggregations for charts.

现在，您可以将度量标准聚合应用于Y轴，将存储桶聚合应用于X轴。 这是图表的所有可用聚合的列表 。

Optionally, you can customize the colors of the visualization.

(可选)您可以自定义可视化的颜色。

Filtering is another mighty feature of Elasticsearch and Kibana. It provides a way to visualize only a selected subset of documents.

过滤是Elasticsearch和Kibana的另一个强大功能。 它提供了一种仅可视化所选文档子集的方法。

See how you can apply filters to the fields based on logical conditions:

了解如何根据逻辑条件将过滤器应用于字段：

As you see, Kibana provides a straightforward way of filtering the data via the comfy interface. Along with that, you can choose how to filter the data — either by using the Kibana Query Language (a simplified query syntax) or Lucene.

如您所见，Kibana提供了一种通过舒适界面过滤数据的简单方法。 除此之外，您可以选择如何过滤数据-通过使用Kibana查询语言 (一种简化的查询语法)或Lucene 。

To allow end-users to filter the data interactively, you can add control widgets — special elements of the dashboard which allow filtering the data simply by clicking them.

为了允许最终用户以交互方式过滤数据，您可以添加控件控件-仪表板的特殊元素，只需单击鼠标控件就可以过滤数据。

Another feature I’d like to highlight is the advanced filtering by dates and the ability to set time intervals for refreshing the data in the dashboard.

我要强调的另一个功能是按日期进行的高级筛选，以及能够设置时间间隔以刷新仪表板中数据的功能。

The good thing is that visualizations are reusable. After creating it, you can save your result and add it to the dashboard any time as well as share with your colleagues given they have access to your Kibana instance.

好处是可视化是可重用的 。 创建结果后，您可以保存结果并将其随时添加到仪表板，并在同事有权访问您的Kibana实例时与同事共享 。

After arranging all the visualization elements on a single page, you can export the final dashboard to PNG or PDF format. This is what makes the dashboards portable — it’s easy to share them across departments in no time.

将所有可视化元素排列在单个页面上之后，您可以将最终的仪表板导出为PNG或PDF格式。 这就是使仪表板具有可移植性的原因-轻松地在各个部门之间轻松共享它们。

Let’s look at an example of the dashboard you can create:

让我们看一下您可以创建的仪表板示例：

To my mind, the principal features which make each dashboard special are interactivity and expressiveness. With it, you can communicate business metrics efficiently.

在我看来，使每个仪表板与众不同的主要特征是交互性和表达性 。 借助它，您可以有效地传达业务指标。

个人印象 (Personal impression)

The visualizations in Kibana ideally perform the tasks they are designed for. What is more, all the visualizations are eye-catching and you can tailor them according to your design ideas. The entire process of creating a dashboard in Kibana is meant to be fast and efficient — and it is so due to the Kibana’s user-friendly and intuitive interface.

Kibana中的可视化工具可以理想地执行其设计任务。 此外，所有的可视化效果都非常抢眼 ，您可以根据自己的设计思路对其进行定制。 在Kibana中创建仪表板的整个过程是为了快速而高效 -正是由于Kibana的用户友好和直观界面。

On the other hand, I’ve felt that some functionality is missing here.

另一方面，我感觉这里缺少一些功能。

When working with data, one of the effective exploratory techniques you can apply is slicing and dicing your data before getting to know which aspects of the data to pay attention to. To my mind, the data table widget isn’t the best option — it presents the data in a flat table which doesn’t support a multi-dimensional view of the data. But playing with data should be done interactively and fast.

在处理数据时，您可以应用的一种有效的探索性技术是对数据进行切片和切块，然后再知道要注意数据的哪些方面。 在我看来，数据表小部件并不是最好的选择-它在不支持数据多维视图的平面表中显示数据。 但是处理数据应该交互且快速地完成。

And this is where a pivot table control comes into play. After searching for available solutions, my choice fell on one open-source plugin called Flexmonster. It handles connecting to the Elasticsearch index and allows creating tabular reports based on the data from its documents. Along with that, integrating with Kibana is smooth — the only thing required to get started is to install a plugin by running one line of code in the command line. You can find more details on GitHub. Before using it, I recommend making sure that your Kibana and Elasticsearch instances are of the same version.

这就是数据透视表控件起作用的地方。 在寻找可用的解决方案之后，我选择了一个名为Flexmonster的开源插件 。 它处理与Elasticsearch索引的连接，并允许根据其文档中的数据创建表格报告 。 除此之外，与Kibana的集成也很流畅-入门所需的唯一操作就是通过在命令行中运行一行代码来安装插件。 您可以在GitHub上找到更多详细信息。 在使用它之前，我建议确保您的Kibana和Elasticsearch实例具有相同的版本。

Once you set up a tool, you are ready to use all available features for searching in-depth insights.

设置工具后，就可以使用所有可用功能来搜索深入的见解。

分析和报告功能 (Features for analytics and reporting)

Flexmonster Pivot provides fast access to the most essential reporting functionality. Its toolbar allows connecting to the data source, loading previously saved reports, exporting reports to PDF, Excel, HTML, CSV, and images. Besides, I’ve managed to quickly switch between two different modes — the grid and the charts. Cells formatting options include conditional and number formatting. The field list deserves particular attention — here you can select hierarchies to rows, columns, measures, and report filters. There is also the search input field which is helpful if the index has a long list of fields.

Flexmonster Pivot提供对最基本报告功能的快速访问。 其工具栏允许连接到数据源，加载先前保存的报告，将报告导出为PDF ， Excel ， HTML ， CSV和图像。 此外，我设法在两种不同的模式之间快速切换-网格和图表。 单元格格式选项包括条件 格式和数字格式 。 字段列表值得特别注意-您可以在此处选择行，列，度量和报告过滤器的层次结构。 还有一个搜索输入字段 ，如果索引的字段列表很长，这将很有帮助。

One of the features I’d like to highlight is the ability to drag and drop the hierarchies right on the grid. Thereby, you can change the slice completely via the UI.

我要强调的功能之一是能够将层次结构直接拖放到网格上。 因此，您可以通过UI完全更改切片。

Another one is the drill-through feature — it helps to know which records stand behind the aggregated values.

另一个功能是追溯功能-它有助于了解哪些记录位于汇总值的后面。

使用数据透视表 (Working with a pivot table)

Let me show you how to create a report based on the Elasticsearch data:

让我向您展示如何基于Elasticsearch数据创建报告：

While testing the tool, I’ve managed to aggregate and filter the data, sort the values on the grid and save the results to continue working with the report later. Plus, exporting works well — it’s easy to share the reports with teammates.

在测试该工具时，我设法汇总和过滤了数据， 对网格上的值进行了排序 并保存结果，以便以后继续使用该报告。 另外，导出效果很好-与队友轻松共享报告。

汇集全部 (Bringing it all together)

Today I’ve covered the benefits Kibana provides for visualization of Elasticsearch data. You’ve been able to make sure how dashboards can empower the analysis process.

今天，我已经介绍了Kibana为Elasticsearch数据的可视化提供的好处。 您已经能够确保仪表板如何支持分析过程。

To my mind, a pivot table is a good tool which enables you to benefit from exploring data before teasing out the answers to complex questions.

在我看来，数据透视表是一个很好的工具，可让您在研究复杂问题的答案之前受益于探索数据。

Flexmonster nicely complements the available functionality of Kibana - the reports you are creating with it are insightful, customizable and can be easily shared across departments.

Flexmonster很好地补充了Kibana的可用功能-您使用它创建的报告具有洞察力，可自定义并且可以在各个部门之间轻松共享。

Working together, both tools have all the potential to boost your storytelling.

两种工具一起使用，都有潜力提升您的讲故事的能力。

I encourage you to give such a combination a try.

我鼓励您尝试这样的组合。

下一步是什么？ (What’s next?)

• Reporting with Kibana

  与Kibana汇报

• Creating a visualization in Kibana

  在Kibana中创建可视化

• Pivot Table for Elasticsearch

  Elasticsearch数据透视表

• How to add a Pivot Table to Kibana

  如何将数据透视表添加到Kibana

翻译自: https://www.freecodecamp.org/news/powerful-tools-for-elasticsearch-data-visualization-analysis/