Showing posts with label xml. Show all posts
Showing posts with label xml. Show all posts

Using GrovePi with Raspberry Pi and MiNiFi Agents for Data Ingest to Parquet, Kudu, ORC, Kafka, Hive and Impala

Using GrovePi with Raspberry Pi and MiNiFi Agents for Data Ingest

Source Code:

Acquiring sensor data from Grove sensors is easy using a GrovePi Hat and some compatible sensors.

Just before my talk at the Future of Data Meetup @ Bell Works in Holmdel, NJ, I thought I should ingest some data from a grove sensor interface.

It's so easy a sleeping cat could do it.

So what does this device look like?  

I have a temperature and humidity sensor on there.

The distance sonic sensor is in there too, that's for the next article.

Let's do this with minimal RAM.

That's a 64GB hard drive underneath in the white case with the RPI.

I need more data and BACON.

We design our MiNiFi Agent Flow in CEM/EFM.   Grab JSON data stream and run sensors.

Apache NiFi 1.9.2 / CFM 1.0 Received HTTPS S2S Events From MiNiFi Agent

A simple flow to query and convert our JSON data, then store it to Kudu and HDFS (ORC) as well as push it to Kafka with a schema.

Let's read that Kafka message and store to Parquet, we will push to MQTT and JMS in the next article.   This is our universal proxy/gateway.

We could infer a schema and not save it.   But by saving a schema to the schema registry it makes SMM, Kafka, NiFi and others schema aware and easy to automagically query and convert between CSV/JSON/XML/AVRO/Parquet and more.

Let's store the data in Parquet files on HDFS with an Impala table.   In Apache NiFi 1.10 there is a ParquetWriter

Before we push to Kafka, let's create a topic for it with Cloudera SMM

Let's build an impala table for that Kudu data.

We can query our tables with ease as data rapidly is added.

Let's Examine the Parquet Files that NiFi Generated

 Let's query that parquet data with Impala in Hue

 Let's monitor that data in Kafka with Cloudera SMM

That was easy from device to enterprise cloud data store(s) with enterprise messages, security, governance, lineage, data catalog, SDX, monitoring and more.   How easy can you ingest IoT data, query it mid stream and store it in multiple data stores.   It took longer to write the article then to do the project and code.   All graphical, Single Sign On, multiple schemas/verisons/data types/engines, multiple OSs, edge, cloud and laptop.   Easy.

Table DDL

(humidity STRING, uuid STRING, systemtime STRING, runtime STRING, cpu DOUBLE, id STRING, te STRING, host STRING, `end` STRING, 
macaddress STRING, temperature STRING, diskusage STRING, memory DOUBLE, ipaddress STRING, host_name STRING) 
LOCATION '/tmp/grovesensors'

CREATE TABLE grovesensors ( uuid STRING,  `end` STRING,humidity STRING, systemtime STRING, runtime STRING, cpu DOUBLE, id STRING, te STRING, 
host STRING,
macaddress STRING, temperature STRING, diskusage STRING, memory DOUBLE, ipaddress STRING, host_name STRING,
PRIMARY KEY (uuid, `end`)
TBLPROPERTIES ('kudu.num_tablet_replicas' = '1')

hdfs dfs -mkdir -p /tmp/grovesensors
hdfs dfs -mkdir -p /tmp/groveparquet

 diskusage STRING, 
  memory DOUBLE,  host_name STRING,
  systemtime STRING,
  macaddress STRING,
  temperature STRING,
  humidity STRING,
  cpu DOUBLE,
  uuid STRING,  ipaddress STRING,
  host STRING,
  `end` STRING,  te STRING,
  runtime STRING,
LOCATION '/tmp/groveparquet/'

Parquet Format

message org.apache.nifi.grove {
  optional binary diskusage (STRING);
  optional double memory;
  optional binary host_name (STRING);
  optional binary systemtime (STRING);
  optional binary macaddress (STRING);
  optional binary temperature (STRING);
  optional binary humidity (STRING);
  optional double cpu;
  optional binary uuid (STRING);
  optional binary ipaddress (STRING);
  optional binary host (STRING);
  optional binary end (STRING);
  optional binary te (STRING);
  optional binary runtime (STRING);
  optional binary id (STRING);


Advanced XML Processing with Apache NiFi 1.9.1

Advanced XML Processing with Apache NiFi 1.9.1

With the latest version of Apache NiFi, you can now directly convert XML to JSON or Apache AVRO, CSV or any other format supported by RecordWriters.   This is a great advancement.  To make it even easier, you don't even need to know the schema before hand.   There is a built-in option to Infer Schema.

The results of an RSS (XML) feed converted to JSON and displayed in a slack channel.

Besides just RSS feeds, we can grab regular XML data including XML data that is wrapped in a Zip file (or even in a Zipfile in an email, SFTP server or Google Docs).

Get the Hourly Weather Observation for the United States

Decompress That Zip

 Unpack That Zip into Files

One ZIP becomes many XML files of data.

An example XML record from a NOAA weather station.

Converted to JSON Automagically

Let's Read Those Records With A Query and Convert the results to JSON Records

Read RSS Feed of Travel Warnings

In this one simple example, we are ingesting all of the observed weather from all observation stations in the United States via one downloaded ZIP file containing all of the hourly XMLs.  Apache NiFi can easily acquire this file, decompress it and unpack the files from the zip.   We can then convert all of them as records by inferring their schema and building new records in the output format of our choice.   We can then pull apart values we need or push this new cleaned format to one or more storage options including HBase, HDFS, Hive, SQL Database, MongoDB or elsewhere.  We can also send this data on via Apache Kafka to a streaming engine such as Kafka Streams or Spark Structured Streaming for more processing or joining with other datasets.

QueryRecord is allowing us to write a SQL query such as SELECT * FROM FLOWFILE reading XML records and producing JSON records as a result.  We can change fields or add things like SUMs and AVGs.


Hourly Update