Context Warning: this was written as an internal tutorial for new hires.


This tutorial demonstrates how you can:

  • store CSV data as gzipped file to an S3 bucket
  • use Hive external table to model and query the data in the S3 bucket
  • use Presto to query the same data using Hive’s metadata

This tutorial assumes you have a basic understanding of Hadoop, Hive, AWS EMR, S3, and Presto.


  • set up an EMR cluster
    • ask sysadmin if you don’t know how to do it, or you don’t have access to one
    • make sure the EMR cluster has both Hive and Presto installed on it. you can do it using AWS console or command line, e.g. argument like aws emr create-cluster --applications Name=Hadoop Name=Hive Name=Presto-Sandbox ...
  • setup passwordless ssh login to the EMR master host
    • if you have a key-pair of choice set up during the cluster creation time then you are all set
    • otherwise you’ll need your own public key which most probably is ~/.ssh/
    • you might also want to check for the rules for SSH (i.e. port 22) in the cluster master’s security group
  • set up AWS CLI client locally
    • you’ll need to do brew install awscli or pip install awscli if you don’t have the client
    • you’ll need to do aws configure to setup the credential
  • make sure you have access to S3
    • try aws s3 ls to list all buckets, for testing purpose
    • if you can’t access S3, check IAM or ask sysadmin

Uploading data to S3

  • create an S3 bucket if you don’t have one, e.g.:

    aws s3 mb s3://bucket-name

  • create a directory that will sync to the bucket, e.g.:

    mkdir ~/Desktop/bucket-folder

  • create a folder under the directory that represents a table (a collection of data), e.g.:

    mkdir ~/Desktop/bucket-folder/employees

  • adding a sample csv data to the folder, e.g.:

    echo 'hilary,female
    trump,male' | gzip > ~/Desktop/bucket-folder/employees/
  • sync the folder with S3, e.g.:

    aws s3 sync ~/Desktop/bucket-folder s3://bucket-name

  • make sure you have the data successfully uploaded, e.g.:

    aws s3 ls s3://bucket-name/employees

Defining and querying Hive table

  • ssh to the Hadoop master machine:

    ssh -i key.pem hadoop@your-hadoop-master-machine

  • enter Hive CLI:


  • create an external table matching your data schema:

    create external table employees (
      name varchar(100),
      gender varchar(10)
    row format delimited
    fields terminated by ','
    lines terminated by '\n'
    location 's3://bucket-name/employees/';
  • now you can try to query the Hive table:

    select * from employees;

  • your SQL should return 2 rows

Appending more data

  • keep the Hive shell open
  • now you can open up another local shell, and append more data to the folder:

    echo 'superman,unknown
    batman,male' | gzip > ~/Desktop/bucket-folder/employees/
  • sync the new data to S3:

    aws s3 sync ~/Desktop/bucket-folder s3://bucket-name

  • go back the hive shell and do another query:

    select * from employees;

  • your SQL should return 4 rows

Presto against the same table

Once you have the table defined in Hive, you can also use Presto to query it. Using presto is as simple as:

  • enter Presto client by typing presto-cli on the cluster’s master machine
  • switch to Hive catalog and default schema: use hive.default;
  • show tables;
  • select * from employees;

Play along, try some other things like select count(*) from employees; which incurs map-reduce job in Hive’s case but not in Presto’s. You’ll find the speed difference between two query engines.


We’ve seen how you can:

  • define an external table pointing to an S3 folder from Hive, and query data stored in there (i.e. on the fly, remembering Hive is a schema-on-read system)
  • append data to a Hive table by simply adding more files (of the same structure) to the same S3 folder
  • use Presto to query the same table (since Presto uses to Hive Metastore)

If you are curious, try some more things with this setting, e.g.:

  • delete a file from that S3 folder, what will happen to the Hive table?
  • what about replacing a file with different content?
  • what about adding some more files with wrong schema/format?
  • upload lines of JSON data and define a schema on it
    • you might want to search for Json Serde
    • Hive does support map, array, and struct which you might find handy
  • upload a larger file, and benchmark the speed of Hive and Presto with more realistic dataset and queries