Ingesting JMS Data into Hive A company has a lot of data transmitting around the enterprise asynchronously with Apache ActiveMQ, they want to tap into it and convert JSON messages coming from web servers and store into Hadoop. I am storing the data into Apache Phoenix / HBase via SQL. And also since it's so easy, I am storing the data into ORC files in HDFS for Apache Hive access. Apache NiFi 1.2 generates DDL for a Hive Table for us hive.ddl CREATE EXTERNAL TABLE IF NOT EXISTS meetup (id INT, first_name STRING, last_name STRING, email STRING, ip_address STRING, company STRING, macaddress STRING, cell_phone STRING) STORED AS ORC LOCATION '/meetup' insert into meetup (id , first_name , last_name , email , ip_address , company , macaddress , cell_phone) values (?,?,?,?,?,?,?,?) HDF Flow ConsumeJMS Path 1: Store in Hadoop as ORC with a Hive Table InferAvroSchema: get a schema from the JSON data ConvertJSONtoAVRO: build an AVRO file from JSON data MergeContent: build a larger chunk of AVRO data ConvertAvroToORC: build ORC files PutHDFS: land in your Hadoop data lake Path 2: Upsert into Phoenix (or any SQL database) EvaluateJSONPath: extract fields from the JSON file UpdateAttribute: Set SQL fields ReplaceText: Create SQL statement with ?. PutSQL: Send to Phoenix through Connection Pool (JDBC) Path 3: Store Raw JSON in Hadoop PutHDFS: Store JSON data on ingest Path 4: Call Original REST API to Obtain Data and Send to JMS GetHTTP: call a REST API to retrieve JSON arrays SplitJSON: split the JSON file into individual records PutJMS <or> PublishJMS: two ways to push messages to JMS. One uses a JMS controller and another uses a JMS client without a controller. I should benchmark this. Error Message from Failed Load If I get errors on JMS send, I send the UUID of the file to Slack for ChatOps. Zeppelin Display of SQL Data To check the tables I use Apache Zeppelin to query Phoenix and Hive tables. Formatting in UpdateAttribute for SQL Arguments To set the ? properties for the JDBC prepared statements, we go by #, starting from 1. The type is the JDBC Type, 12 is String; and the value is the value of the FlowFile fields. Yet another message queue ingested with no fuss. ... View more

Backup Files from Hadoop ListHDFS set parameters, pick a high level directory and work down. /etc/hadoop/conf/core-site.xml FetchHDFS ${path}/${filename} PutFile Store in local Backup Hive Tables SelectHiveQL Output format AVRO, with SQL: select * from beaconstatus ConvertAVROtoORC generic for all the tables UpdateAttribute tablename ${hive.ddl:substringAfter('CREATE EXTERNAL TABLE IF NOT EXISTS '):substringBefore(' (')} PutFile Use replace directories and create missing directories with directory: /Volumes/Transcend/HadoopBackups/hive/${tablename} For Phoenix tables, I use the same ConvertAvroToORC, UpdateAttribute and PutFile boxes and just add ExecuteSQL to ingest Phoenix data. For every new table, I add one box and link it to ConvertAvroToORC. Done! This is enough of a backup so if I need to rebuild and refill my development cluster, I can do so easily. Also I have them schedule for once a day to rewrite everything. This is Not For Production or Extremely Large Data! This works great for a development cluster or personal dev cluster. You can easily backup files by ingesting with GetFile and other things can be backed up by called ExecuteStreamCommand. Local File Storage of Backed up Data drwxr-xr-x 3 tspann staff 102 May 20 23:00 any_data_trials2 drwxr-xr-x 3 tspann staff 102 May 20 22:59 any_data_meetup drwxr-xr-x 3 tspann staff 102 May 20 22:59 any_data_ibeacon drwxr-xr-x 3 tspann staff 102 May 20 22:57 any_data_gpsweather drwxr-xr-x 3 tspann staff 102 May 20 10:53 any_data_beaconstatus drwxr-xr-x 3 tspann staff 102 May 20 10:52 any_data_beacongateway drwxr-xr-x 3 tspann staff 102 May 19 17:36 any_data_atweetshive2 drwxr-xr-x 3 tspann staff 102 May 19 17:31 any_data_atweetshive Other Tools to Extract Data ShowTables to get your list and then you can grab all the DDL for the Hive tables. ddl.sql show create table atweetshive; show create table atweetshive2; show create table beacongateway; show create table beaconstatus; show create table dronedata; show create table gps; show create table gpsweather; show create table ibeacon; show create table meetup; show create table trials2; Hive Script to Export Table DDL beeline -u jdbc:hive2://myhiveserverthrift:10000/default --color=false --showHeader=false --verbose=false --silent=true --outputformat=csv -f ddl.sql Backup Zeppelin Notebooks in Bulk tar -cvf notebooks.tar /usr/hdp/current/zeppelin-server/notebook/ gzip -9 notebooks.tar scp userid@pservername:/opt/demo/notebooks.tar.gz . ... View more

Raspberry Pi Killer? Nope, but this device has twice the RAM and a bit more performance. It's mostly compatible with Pi, but not fully. It is very new and has little ecosystem but can get the job done. Device Setup It is easy to install Python and all the libraries required for IoT and some deep learning. I found most instructions worked for Raspberry Pi on this device. It has more RAM which helps on some of these activities. I downloaded and burned with Etcher a MicroSD image of TinkerOS_Debian V1.8 (Beta version). It's a Debian variant close enough to Raspian for most IoT developers and users to be comfortable. An Android OS is now available for download as well and that may be worth trying, I am wondering if Google will add this device to the AndroidThings supported devices? Perhaps. One quirk, make sure you remember this: TinkerOS default username is “linaro”, password is “linaro”. Connect to the device via ssh linaro@SOMEIP. Python Setup sudo apt-get update sudo apt-get install cmake gcc g++ libxml2 libxml2-* leveldb* sudo apt-get install python-dev python3-dev sudo apt-get install python-setuptools sudo apt-get install python3-setuptools pip install twython pip install numpy pip install wheel pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose sudo pip install -U nltk python import nltk nltk.download() quit() pip install -U spacy python -m spacy.en.download all sudo python -m textblob.download_corpora # TensorFlow get https://github.com/samjabrahams/tensorflow-on-raspberry-pi/releases/download/v1.0.1/tensorflow-1.0.1-cp27-none-linux_armv7l.whl sudo pip install tensorflow-1.0.1-cp27-none-linux_armv7l.whl # For Python 3.4 wget https://github.com/samjabrahams/tensorflow-on-raspberry-pi/releases/download/v1.0.1/tensorflow-1.0.1-cp34-cp34m-linux_armv7l.whl sudo pip3 install tensorflow-1.0.1-cp34-cp34m-linux_armv7l.whl # For Python 2.7 sudo pip uninstall mock sudo pip install mock # For Python 3.4 sudo pip3 uninstall mock sudo pip3 install mock sudo apt-get install git git clone https://github.com/tensorflow/tensorflow.git # PAHO for MQTT pip install paho-mqtt # Flask for Web Apps pip install flask Python 2.7 and 3.4 both work fine on this device, I was also able to install the major NLP libraries including SpaCy and NLTK. TensorFlow installed using the Raspberry PI build and ran without incident. I believe it's a bit faster than the RPI version. I will have to run some tests on that. Run The Regular TensorFlow Inception V3 Demo python -W ignore /tensorflow/models/tutorials/image/imagenet/classify_image.py --image_file /opt/demo/tensorflow/TimSpann.jpg I hacked that version to add code to send the results to MQTT so I could process with most IoT hubs and Apache NiFi with ease. JSON is a very simple format to work with. Custom Python to Call NiFi # .... imports import paho.mqtt.client as paho import os import json # .... later in the code top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1] for node_id in top_k: human_string = node_lookup.id_to_string(node_id) score = predictions[node_id] print('==> %s (score = %.5f)' % (human_string, score)) row = [ { 'human_string,': str(human_string), 'score,': str(score)} ] json_string = json.dumps(row) client = paho.Client() client.connect("192.168.1.151", 1883, 60) client.publish("tinker1", payload=json_string, qos=0, retain=True) NIFI Ingest Ingesting MQTT is easy and again that's our choice from the TinkerBoard. I have formatted the TensorFlow data as JSON and we quickly ingest and drop to a file. We could do anything with this flow file include store in Hadoop, Hive, Phoenix, HBase or send it to Kafka or transform it. So now we have yet another platform that can be used for IoT and basic Deep Learning and NLP. All enabled by a small fast linux device that runs Python. Enjoy your SBC! I am hoping that they add hats, a hard drive and some other ASUS accessories. Make your own mini Debian laptop would be cool. The next device I am looking at is NVIDIA's powerful GPU SBCs. There's a couple options from 192 GPU cores up to 256 with smoking high-end specs. Example Data [{"score,": "0.034884", "human_string,": "neck brace"}] Downloads ASUS SBC Download TinkerBoard FAQ Scripts Modified TensorFlow example /models/tutorials/image/imagenet/classify_image.py ... View more