INGESTING RDBMS DATA I previously posted an article on ingesting and converting data (https://community.hortonworks.com/articles/64069/converting-a-large-json-file-into-csv.html). Once you have a SQL database loaded, you will eventually need to store your data in your one unified datalake. This is quite simple with NiFi. If you have a specialized tool that reads from your RDBMS logs and sends them to Kafka or JMS, that would be easy to ingest as well. For those wishing to stay open source, NiFi works great. If you don't have a good increasing key to use, you can add an article one that increases on every insert. Almost every database supports this from MariaDB to Oracle. ALTER TABLE `useraccount` ADD COLUMN `id` INT AUTO_INCREMENT UNIQUE FIRST; For mine, I just added an autoincrement id column to be my trigger. For Apache NiFi, you will need connections to all your sources and sinks. So I need a DB Connection Pool for Apache Phoenix and MySQL (DBCPConnectionPool) as well as Hive (HiveConnectionPool). Tools Required:
RDMS (I am using MySQL) HDF 2.0 (NiFi 1.0.0+) HDP 2.4+ (I am using HDP 2.5) with HBase and Phoenix enabled and running, HDFS, YARN and Hive running. Optional: Apache Zeppelin for quick data analysis and validation To build a SQL database, I needed a source of interesting and plentiful data. So I used the excellent free API: https://api.randomuser.me/. It's easy to get this URL to return 5,000 formatted JSON results via the extra parameters: ?results=3&format=pretty. This API returns JSON in this format that requires some basic transformation (easily done in NiFi). {"results":[
{"gender":"male",
"name":{"title":"monsieur","first":"lohan","last":"marchand"},
"location":{"street":"6684 rue jean-baldassini","city":"auboranges","state":"schwyz","postcode":9591},
"email":"lohan.marchand@example.com",
"login":{"username":"biggoose202","password":"esther","salt":"QIU1HBsr","md5":"9e60da6d4490cd6d102e8010ac98f283","sha1":"3de3ea419da1afe5c83518f8b46f157895266d17","sha256":"c6750c1a5bd18cac01c63d9e58a57d75520861733666ddb7ea6e767a7460479b"},
"dob":"1965-01-28 03:56:58",
"registered":"2014-07-26 11:06:46",
"phone":"(849)-890-5523",
"cell":"(395)-127-9369",
"id":{"name":"AVS","value":"756.OUVK.GFAB.51"},
"picture":{"large":"https://randomuser.me/api/portraits/men/69.jpg","medium":"https://randomuser.me/api/portraits/med/men/69.jpg","thumbnail":"https://randomuser.me/api/portraits/thumb/men/69.jpg"},"nat":"CH"}]
Then I created a MySQL table to populate with JSON data. drop table useraccount; create table useraccount(
gender varchar(200),
title varchar(200),
first varchar(200),
last varchar(200),
street varchar(200),
city varchar(200),
state varchar(200),
postcode varchar(200),
email varchar(200),
username varchar(200),
password varchar(200),
salt varchar(200),
md5 varchar(200),
sha1 varchar(200),
sha256 varchar(200),
dob varchar(200),
registered varchar(200),
phone varchar(200),
cell varchar(200),
name varchar(200),
value varchar(200),
large varchar(200),
medium varchar(200),
thumbnail varchar(200),
nat varchar(200)); I created a Phoenix table ontop of HBase to hold data: create table useraccount(
gender varchar,
title varchar,
firstname varchar,
lastname varchar,
street varchar,
city varchar,
state varchar,
postcode varchar,
email varchar,
username varchar,
password varchar,
salt varchar,
md5 varchar not null primary key,
sha1 varchar,
sha256 varchar,
dob varchar,
registered varchar,
phone varchar,
cell varchar,
name varchar,
value2 varchar,
large varchar,
medium varchar,
thumbnail varchar,
nat varchar); Step 1: QueryDatabaseTable Reads from MySQL tables. This processor just needs the MySQL Connection, table name: useraccount and column: id. With have two forks from this query table. Fork 1 Step 2: ConvertAvroToJSON Use Array You will get arrays of JSON that look like this: {
"id" : 656949,
"gender" : "female",
"title" : "madame",
"first" : "amandine",
"last" : "sanchez",
"street" : "8604 place paul-duquaire",
"city" : "savigny",
"state" : "genève",
"postcode" : "5909",
"email" : "amandine.sanchez@example.com",
"username" : "ticklishmeercat183",
"password" : "hillary",
"salt" : "Sgq7HHP1",
"md5" : "d82d6c3524f3a1118399113e6c43ed31",
"sha1" : "23ce2b372f94d39fb949d95e81e82bece1e06a4a",
"sha256" : "49d7e92a2815df1d5fd991ce9ebbbcdffee4e0e7fe398bc32f0331894cae1154",
"dob" : "1983-05-22 15:16:49",
"registered" : "2011-02-06 22:03:37",
"phone" : "(518)-683-8709",
"cell" : "(816)-306-5232",
"name" : "AVS",
"value" : "756.IYWK.GJBH.35",
"large" : "https://randomuser.me/api/portraits/women/50.jpg",
"medium" : "https://randomuser.me/api/portraits/med/women/50.jpg",
"thumbnail" : "https://randomuser.me/api/portraits/thumb/women/50.jpg",
"nat" : "CH"
} Step 3: SplitJSON Use: $.* to split all the arrays into individual JSON records. Step 4: EvaluateJSONPath You need to pull out each attribute you want and name it, example cell for $.cell See the guide to JSONPath with testing tool here. Step 5: ReplaceText Here we format the SQL from the attributes we just parsed from JSON: upsert into useraccount (gender,title,firstname,lastname,street,city,state,postcode,email,
username,password,salt,md5,sha1,sha256,dob,registered,phone,cell,name,value2,large,medium,thumbnail,nat)
values ('${'gender'}','${'title'}','${'first'}','${'last'}','${'street'}','${'city'}','${'state'}','${'postcode'}',
'${'email'}','${'username'}','${'password'}','${'salt'}','${'md5'}','${'sha1'}','${'sha256'}','${'dob'}',
'${'registered'}','${'phone'}','${'cell'}','${'name'}','${'value'}','${'large'}','${'medium'}','${'thumbnail'}','${'nat'}' )
Step 6: PutSQL With an example Batch Size of 100, we connect to our Phoenix DB Connection Pool. Fork 2 Step 2: UpdateAttribute We set orc.table to useraccount Step 3: ConvertAvroToORC We set our configuration files for Hive: /etc/hive/conf/hive-site.xml, 64MB stripe, and importantly Hive Table Name to ${orc.table} Step 4: PutHDFS Set out configuration /etc/hadoop/conf/core-site.xml and a directory you have access to write to for storing the ORC files. Step 5: ReplaceText Search Value: (?s:^.*$) Replacement Value: ${hive.ddl} LOCATION '${absolute.hdfs.path}' Always replace and entire text. Step 6: PutHiveQL You need to connect to your Hive Connection. You will see the resulting ORC files in your HDFS directory
[root@tspanndev12 demo]# hdfs dfs -ls /orcdata
Found 2 items
-rw-r--r-- 3 root hdfs 246806 2016-10-29 01:24 /orcdata/2795061363634412.orc
-rw-r--r-- 3 root hdfs 246829 2016-10-29 17:25 /orcdata/2852682816977877.orc
After my first few batches of data are ingested, I check them in Apache Zeppelin. Looks good. The data has also been loaded into Apache Hive.
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