is it possible to read mongodb data with hadoop connector but save output as mysql data table. So I want to read some data from mongodb collection by hadoop, processing it with hadoop and outputing it NOT already in mongodb but as MYSQL.
I used like, fetching data from mongodb as input and store result in different mongodb address. For that one you need to specify like
MongoConfigUtil.setInputURI(discussConf,"mongodb://ipaddress1/Database.Collection");
MongoConfigUtil.setOutputURI(discussConf,"mongodb://ipaddress2/Database.Collection");
for mongodb to mysql
my suggestion is , you can write normal java code to insert whatever data you need to insert in mysql . that code may be in reduce or map function
Related
I am new to Azure. I have a requirement to transfer data from a table in a transaction MySqlDb which is stored in json blobs(I am not aware why they have used a nosql format writes in a sql db) to a table in PostgreSql but in a flattened format. What is the best way to achieve this? This is not a one time task, but needs to be done everytime there is a ingestion to the transaction db and I need to push those records into postgresql db.
What you need is an ETL (Extract - Transform - Load) tool. The one available on Azure is the Azure Data Factory, which has connectors for MySQL, PostGreSQL and many more. So basically you'll create a pipeline, use the copy data activity to extract the data from MYSQL and then insert it into PostGreSQL
You can get more information in here:
https://learn.microsoft.com/en-us/azure/data-factory/connector-mysql
I have a lot of data (JSON string) per day (around 150-200B).
I want to insert the JSON to Hadoop, what is the best way to do it (I need a fast insert and a fast query on JSON fields)?
Do I need to use hive and create Avro scheme to my JSON? Or do I need to insert the JSON as a string to a specific column?
If you want to make the data available in Hive to perform mostly aggregations on top of it, I would suggest 1 of the following method using spark.
If you have multiple-line json files
var df = spark.read.json(sc.wholeTextFiles("hdfs://ypur/hdfs/path/*.json").values)
df.write.format("parquet").mode("overwrite").saveAsTable("yourhivedb.tablename")
If you have single-line json files
val df = spark.read.json("hdfs://ypur/hdfs/path/*.json")
df.write.format("parquet").mode("overwrite").saveAsTable("yourhivedb.tablename")
Spark will automatically infer the table schema for you. If you are using cloudera distribution you will be able to read the data using impala (depending on your cloudera version it may not support complex structures)
I want to insert the JSON to Hadoop
You just put it in HDFS... Since you have data over a time period, you'll want to create partitions for Hive to read
jsondata/dt=20180619/foo.json
jsondata/dt=20180620/bar.json
Do I need to use hive and create Avro scheme to my JSON?
Nope. Not sure where you got mixed up between Avro and JSON. Now, if you could convert the JSON into defined Avro with a schema, then that would help improve Hive queries since querying structured binary is better than parsing JSON text.
do I need to insert the JSON as a string to a specific column?
Not recommended. You could, but then you cannot query it, via Hive's JSON Serde support
Don't forget with the above structure you'll need PARTITIONED BY (dt STRING). And in order for partitions to be created on the table for existing files, you'll need to manually (and daily) run an MSCK REPAIR TABLE command
i have JSON as string (from kafka)
Don't use Spark for that (at least, don't reinvent the wheel). My suggestion would be to use Confluent's HDFS Kafka Connect that comes with Hive table creation support.
I have no experience dealing with nosql databases such as Amazon AWS DynamoDB.
I have some data stored in Amazon AWS DynamoDB.
Is it possible to export data from DynamoDB to MySQL Server ?
If so, how to go about accomplishing that ?
Thanks,
I would extract the data in CSV format. This "DynamoDBtoCSV" tool seems promising. Then you can import this CSV file into your MySQL database with LOAD DATA INFILE.
The drawback is that you 1. need to create the receiving structure first and 2. repeat the process for each table. But it shouldn't be too complicated to 1. generate a corresponding CREATE TABLE statement from the first line output by DynamoDBtoCSV, and 2. run the operation in a loop from a batch.
Now I am asking myself if MySQL is your best call as a target database. MySQL is a relational database, while DynamoDB is NoSQL (with variable length aggregates, non-scalar field values, and so on). Flatenning this structure into a relational schema may not be such a good idea.
Even though it is pretty old question, still leaving it here for future researchers.
Dynamodb supports streams which can be enabled on any table (from overview section in dynamodb table), which then can be taken via a lambda function (look for trigger tab in dynamodb table) to any storage including but not limited to mysql.
Data flow:
Dynamodb update/insert > Stream > Lambda > Mysql.
Is there is any simple procedure for converting google's nosql to local mysql database ?
I have downloaded all the data using Remote API Bulk Loader .
How can i seperate desired entity from this bulk data .
Later i want to convert all entities to mysql databse.
First downloaded all the data in CSV format using following command ,
appcfg.py download_data --config_file=bulkloader.yaml --filename=users.csv --kind=Permission --url=http://your_app_id.appspot.com/_ah/remote_api
Then used phpmyadmin.
Summary:
Is it possible to:
Import data into Hadoop with the «MongoDB Connector for Hadoop».
Process it with Hadoop MapReduce.
Export it with Sqoop in a single transaction.
I am building a web application with MongoDB. While MongoDB work well for most of the work, in some parts I need stronger transactional guarantees, for which I use a MySQL database.
My problem is that I want to read a big MongoDB collection for data analysis, but the size of the collection means that the analytic job would take too long to process. Unfortunately, MongoDB's built-in map-reduce framework would not work well for this job, so I would prefer to carry out the analysis with Apache Hadoop.
I understand that it is possible read data from MongoDB into Hadoop by using the «MongoDB Connector for Hadoop», which reads data from MongoDB, processes it with MapReduce in Hadoop, and finally outputs the results back into a MongoDB database.
The problem is that I want the output of the MapReduce to go into a MySQL database, rather than MongoDB, because the results must be merged with other MySQL tables.
For this purpose I know that Sqoop can export result of a Hadoop MapReduce into MySQL.
Ultimately, I want too read MongoDB data then process it with Hadoop and finally output the result into a MySQL database.
Is this possible? Which tools are available to do this?
TL;DR: Set an an output formatter that writes to a RDBS in your Hadoop job:
job.setOutputFormatClass( DBOutputFormat.class );
Several things to note:
Exporting data from MongoDB to Hadoop using Sqoop is not possible. This is because Sqoop uses JDBC which provides a call-level API for SQL-based database, but MongoDB is not an SQL-based database. You can look at the «MongoDB Connector for Hadoop» to do this job. The connector is available on GitHub. (Edit: as you point out in your update.)
Sqoop exports are not made in a single transaction by default. Instead, according to the Sqoop docs:
Since Sqoop breaks down export process into multiple transactions, it is possible that a failed export job may result in partial data being committed to the database. This can further lead to subsequent jobs failing due to insert collisions in some cases, or lead to duplicated data in others. You can overcome this problem by specifying a staging table via the --staging-table option which acts as an auxiliary table that is used to stage exported data. The staged data is finally moved to the destination table in a single transaction.
The «MongoDB Connector for Hadoop» does not seem to force the workflow you describe. According to the docs:
This connectivity takes the form of allowing both reading MongoDB data into Hadoop (for use in MapReduce jobs as well as other components of the Hadoop ecosystem), as well as writing the results of Hadoop jobs out to MongoDB.
Indeed, as far as I understand from the «MongoDB Connector for Hadoop»: examples, it would be possible to specify a org.apache.hadoop.mapred.lib.db.DBOutputFormat into your Hadoop MapReduce job to write the output to a MySQL database. Following the example from the connector repository:
job.setMapperClass( TokenizerMapper.class );
job.setCombinerClass( IntSumReducer.class );
job.setReducerClass( IntSumReducer.class );
job.setOutputKeyClass( Text.class );
job.setOutputValueClass( IntWritable.class );
job.setInputFormatClass( MongoInputFormat.class );
/* Instead of:
* job.setOutputFormatClass( MongoOutputFormat.class );
* we use an OutputFormatClass that writes the job results
* to a MySQL database. Beware that the following OutputFormat
* will only write the *key* to the database, but the principle
* remains the same for all output formatters
*/
job.setOutputFormatClass( DBOutputFormat.class );
I would recommend you take a look at Apache Pig (which runs on top of Hadoop's map-reduce). It will output to MySql (no need to use Scoop). I used it to do what you are describing. It is possible to do an "upsert" with Pig and MySql. You can use Pig's STORE command with piggyBank's DBStorage and MySql's INSERT DUPLICATE KEY UPDATE (http://dev.mysql.com/doc/refman/5.0/en/insert-on-duplicate.html).
Use MongoHadoop connector to read data from MongoDB and process it using Hadoop.
Link:
https://github.com/mongodb/mongo-hadoop/blob/master/hive/README.md
Using this connector you can use Pig and Hive to read data from Mongo db and process it using Hadoop.
Example of Mongo Hive table:
CREATE EXTERNAL TABLE TestMongoHiveTable
(
id STRING,
Name STRING
)
STORED BY 'com.mongodb.hadoop.hive.MongoStorageHandler'
WITH SERDEPROPERTIES('mongo.columns.mapping'='{"id":"_id","Name":"Name"}')
LOCATION '/tmp/test/TestMongoHiveTable/'
TBLPROPERTIES('mongo.uri'='mongodb://{MONGO_DB_IP}/userDetails.json');
Once it is exported to hive table you can use Sqoop or Pig to export data to mysql.
Here is a flow.
Mongo DB -> Process data using Mongo DB hadoop connector (Pig) -> Store it to hive table/HDFS -> Export data to mysql using sqoop.