Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL.
This is a simple getting started example that’s based upon “Hive for Beginners”, with what I feel is a bit more useful information.
The installation and configuration of Hadoop and Hive is beyond the scope of this article. If you’re just getting started, I would highly recommend grabbing one of Cloudera’s pre-built virtual machines that have everything you need.
Cloudera’s VMs have changed substantially since this article was written. I have not been able to verify that the new VMs will work with these instructions.
I’m assuming that you will be running the following steps using the Cloudera VM, logged in as the cloudera user. If your setup is different, adjust accordingly.
Step 1: Preparing the Data
head BX-Books.csv to see the first few lines of the raw data. You’ll notice that’s it’s not really comma-delimited; the delimiter is ‘
;’. There are also some escaped HTML entities we can clean up, and the quotes around all of the values can be removed.
The first line in the file looks like this:
This lines defines the data format of the fields in the file. We’ll want to refer back to it later.
Open a terminal and enter:
sed 's/\&/\&/g' BX-Books.csv | sed -e '1d' |sed 's/;/$$$/g' | sed 's/"$$$"/";"/g' | sed 's/"//g' > BX-BooksCorrected.txt
- Replace all
- Remove the first (header) line
- Change all semicolons to
- Change all
- Remove all
Steps 3 and 4 may look strange, but some of the field content may contain semicolons. In this case, they will be converted to
$$$, but they will not match the
"$$$" pattern, and will not be converted back into semicolons and mess up the import process.
Step 2: Importing the Data
Now that we have some normalized data, we need to add it to the Hadoop file system (HDFS) so that Hive can access it. In the terminal, type:
hadoop fs -mkdir input hadoop fs -put /path/to/BX-BooksCorrected.txt input
(Use the correct path to the
Step 3: Running Hive
hive at the console to start Hive.
Once Hive has started, you’ll see the
hive> command prompt. Let’s verify that our file did get loaded into HDFS.
dfs -ls input;
Step 4: Analyzing the Data
Now that we have the data ready, let’s do something with it. The simple example is to see how many books were published per year. We’ll start with that, then see if we can do a bit more.
Load the data into a Hive table:
CREATE TABLE IF NOT EXISTS BookData > (ISBN STRING, > BookTitle STRING, > BookAuthor STRING, > YearOfPublication INT, > Publisher STRING) > ROW FORMAT DELIMITED > FIELDS TERMINATED BY '\;' > STORED AS TEXTFILE; LOAD DATA INPATH '/user/cloudera/input/BX-BooksCorrected.txt' > OVERWRITE INTO TABLE BookData;
(HQL commands are terminated with semicolons. If you press Return on a line without terminating it, you’ll get the
> character, indicating that the command has been continued and not entered yet.)
This creates a Hive table named
BookData and loads the information from HDFS into it. Hive expects data to be tab-delimited by default, but ours is not; we have to tell it that semicolons are field separators by providing the
FIELDS TERMINATED BY argument. You’ll notice that we left off all of the “
Image-URL-XXX” fields; we don’t need them for analysis, and Hive will ignore fields that we don’t tell it to load.
If you want to see what the loaded data structure looks like, you can use the
Finding books by year
We’ll start with the simple analysis of how many books were written by year. In Hive, this can be accomplished with a single query.
Get number of books by year:
SELECT YearOfPublication, COUNT(BookTitle) > FROM BookData GROUP BY YearOfPublication;
You’ll see a listing of years, along with the number of books for that year. You may notice that some of the values don’t make much sense; there should be no year 0, nor should there be entries for a blank year. We’ll clean those problems up in the next analysis.
More Advanced Analysis
There’s a lot more data in the set beyond years and books counts. What if we wanted to see books published per year by author? Why don’t we go a step farther and group those results by publisher as well?
Let’s do a little bit of cleanup on the data to eliminate the unwanted years.
INSERT OVERWRITE TABLE BookData > SELECT BookData.* > FROM BookData WHERE YearOfPublication > 0;
This will only keep records where we have a positive year of publication value.
Generating the final results is again a single query:
SELECT Publisher, BookAuthor, YearOfPublication, COUNT(BookTitle) > FROM BookData > GROUP BY Publisher, BookAuthor, YearOfPublication;
At last! We have our results.
(Want to compare the Hive steps with Pig? Here’s the same example using Pig.)