HDFS data output
This article provides an introduction to HDFS along with a guide on how to create an HDFS data output using Upsolver.
Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation.
It provides a software framework for distributed storage and processing of big data using the MapReduce programming model.
As HDFS provides better data throughput than traditional file systems, in addition to high fault tolerance and native support of large datasets, it is used to scale a single Apache Hadoop cluster to hundreds (and even thousands) of nodes.
1. Go to the Outputs page and click New.
2. Select HDFS as your output type.
3. Name your output and select whether the output should be Tabular or Hierarchical. After adding your Data Sources, click Next.
4. Click the information icon
in the fields tree to view information about a field. The following will be displayed:
Density in Events
Density in Data
How many of the events in this data source include this field, expressed as a percentage (e.g. 20.81%).
The density in the hierarchy (how many of the events in this branch of the data hierarchy include this field), expressed a percentage.
How many unique values appear in this field.
The total number of values ingested for this field.
The first time this field included a value, for example, a year ago.
The last time this field included a value, for example, 2 minutes ago.
Field Content Samples Over Time
The percentage distribution of the field values. These distribution values can be exported by clicking Export.
A time-series graph of the total number of events that include the selected field.
The most recent data values for the selected field and columns. You can change the columns that appear by clicking Choose Columns.
5. Click the information icon
next to a hierarchy element (such as the overall data) to review the following metrics:
# of Fields
# of Keys
# of Arrays
The number of fields in the selected hierarchy.
The number of keys in the selected hierarchy.
The number of arrays in the selected hierarchy.
A stacked bar chart (by data type) of the number of fields versus the density/distinct values or a stacked bar chart of the number of fields by data type.
A list of the fields in the hierarchy element, including Type, Density, Top Values, Key, Distinct Values, Array, First Seen, and Last Seen.
6. Click the plus icon
in the fields tree to add a field from the data source to your output. This will be reflected under the Data Source Field in the Schema tab. If required, modify the Output Column Name.
Toggle from UI to SQL at any point to view the corresponding SQL code for your selected output.
7. Add any required calculated fields and review them in the Calculated Fields tab. See: Adding Calculated Fields
8. Add any required lookups and review them under the Calculated Fields tab.
10. Click Make Aggregated to turn the output into an aggregated output. Read the warning before clicking OK and then add the required aggregation. This aggregation field will then be added to the Schema tab. See: Aggregation Functions
11. In the Aggregation Calculated Fields area under the Calculated Fields tab, add any required calculated fields on aggregations. See: Functions, Aggregation Functions
Click Preview at any time to view a preview of your current output.
12. Click Run and fill out the following fields:
13. Click Next and complete the following:
Processing Time Range
Select the compute cluster to run the calculation on. Alternatively, click the drop-down and create a new compute cluster.
The range of data to process. This can start from the data source beginning, now, or a custom date and time. This can never end, end now, or end at a custom date and time.
14. Finally, click Deploy to run the output. It will show as Running in the output panel and is now live in production and consumes compute resources.
If you have trouble deploying your output, please contact Upsolver for support.