tl;dr into now handles data on remote machines, including HDFS and the Hive Metastore (kinda).

## Motivation

Last week I wrote about into, a library to migrate data between formats. We saw that a network of pairwise data conversions can robustly migrate data, eliminating some of the frustration of data science.

This frustration compounds when data lives on other computers or distributed file systems like HDFS. Moving data from your local machine into something like the Hive metastore often requires several steps.

1. scp data to cluster
2. hadoop fs -cp data to HDFS
3. CREATE TABLE in Hive/Impala to register data with metastore
4. Write SQL queries

While each of these steps may be relatively straightforward, their combination can be daunting to the casual analyst.

## Remote data and into

So we took this as a case study and extended the into network appropriately. We integrate the following libraries and protocols

• ssh://hostname:myfile.csv accesses data on remote machines through paramiko
• hdfs://hostname:myfile.csv accesses data on the Hadoop distributed file system through WebHDFS using the pywebhdfs library
• hive://hostname::tablename accesses data on the Hive Metastore using a combination of SQLAlchemy and hand crafted CREATE TABLE / LOAD statements

## SSH

into is now a fancy scp.

Because we’re connected to the network, lots of other things work too.

Note that we’re not calling any code on the remote machine so fancy conversions always happen locally.

If you’d like to use ssh generally you might want to take a look at Paramiko which is really doing all of the heavy lifting here.

## HDFS

WebHDFS is a web interface to the Hadoop File System. It is surprisingly high performance (I often erroneously think of HTTP as slow) but isn’t always turned on in every instance. If it is then you should be able to transfer data in and out easily, just as we did for SSH

## Hive

The interesting piece comes when we come to Hive, which, in into parlance expects one of the following kinds of data:

ssh://single-file.csv
ssh://directory-of-files/*.csv
hdfs://directory-of-files/*.csv


And so we build these routes, enabling operations like the following:

But Hive is also a bit finicky. Blaze uses the PyHive sqlalchemy dialect to query Hive tables; unfortunately the way Hive works we need to create them by hand. Hive is different from most databases in that it doesn’t have an internal format. Instead, it represents tables as directories of CSV files (or other things). This distinction mucks up into’s approach a bit but things work ok in normal situations.

## Lessons Learned

We had to add a couple new ideas to into to expand out to these systems.

### Type Modifiers

First, we needed a way to refer to different variants of the same format of file. For example, for CSV files we now have the following variants

A local CSV file
A CSV file accessible through HDFS
A CSV file accessible through SSH
A directory of CSV files
A directory of CSV files on HDFS
...


And the same for JSON, text, etc.. Into decides what conversion functions to run based on the type of the data, so in principle we need subclasses for all combinations of format and location. Yuck.

To solve this problem we create functions, SSH, HDFS, Directory to create subclasses, we call these type modifiers. So SSH(CSV) is a new type that acts like a CSV file and like the hidden _SSH superclass.

Note that users don’t usually see these (unless they want to be explicit) they usually interact with uri strings.

### Temporary files

Second, we need a way to route through temporary files. E.g. consider the following route:

SSH(CSV) -> CSV -> pd.DataFrame


Both steps of this path are easy given paramiko and pandas. However we don’t want the intermediate CSV file to hang around (users would hate us if we slowly filled up their /tmp folder.) We need to clean it up when we’re done.

To solve this problem, we introduce a new type modifier, Temp, that drops itself on garbage collection (drop is another magic function in into, see docs). This lets us tie the Python garbage collector to persistent data outside of the Python process. It’s not fool-proof, but it is pretty effective.

SSH(CSV) -> Temp(CSV) -> pd.DataFrame


This is also a good example of how we build type modifiers. You can safely ignore the following code:

I won’t be surprised if this approach concerns a few people but I’ve found it to be effective so far.

### Keyword Arguments

The number of possible keyword arguments to a single into call is increasing. We don’t have a good mechanism to help users discover the valid options for their situation. Docstrings are hard here because the options depend on the source and target inputs. For the moment we’re solving this with online documentation for each complicated format but there is probably a better solution out there.

## Help!

The new behavior around ssh:// and hdfs:// and hive:// is new, error prone, and could really use play-testing. I strongly welcome feedback and error reporting here. You could file an issue or e-mail [email protected]

## Other

I didn’t mention anything about S3 and RedShift support that was also recently merged. This is because I think Phil Cloud might write a separate blogpost about it. We did this work in parallel in an effort to hash out how best to solve the problems above. I think it worked decently well

Also, we’ve added an into command line interface. It works just like the into function with strings, except that we’ve reversed the order of the arguments to be more like cp. An example is below:

$into source target --key value --key value --key value$ into myfile.csv ssh://hostname:myfile.json --delimter ','