Python bindings for hdfs-native Rust library


Keywords
hadoop, hdfs
License
Apache-2.0
Install
pip install hdfs-native==0.2.0

Documentation

Native Rust HDFS client

This is a proof-of-concept HDFS client written natively in Rust. All other clients I have found in any other language are simply wrappers around libhdfs and require all the same Java dependencies, so I wanted to see if I could write one from scratch given that HDFS isn't really changing very often anymore. Several basic features are working, however it is not nearly as robust and the real HDFS client.

What this is not trying to do is implement all HDFS client/FileSystem interfaces, just things involving reading and writing data.

Supported HDFS features

Here is a list of currently supported and unsupported but possible future features.

HDFS Operations

  • Listing
  • Reading
  • Writing
  • Rename
  • Delete

HDFS Features

  • Name Services
  • Observer reads (state ID tracking is supported, but needs improvements on tracking Observer/Active NameNode)
  • ViewFS
  • Router based federation
  • Erasure coded reads and writes
    • RS schema only, no support for RS-Legacy or XOR

Security Features

  • Kerberos authentication (GSSAPI SASL support) (requires libgssapi_krb5, see below)
  • Token authentication (DIGEST-MD5 SASL support)
  • NameNode SASL connection
  • DataNode SASL connection
  • DataNode data transfer encryption
  • Encryption at rest (KMS support)

Kerberos Support

Kerberos (SASL GSSAPI) mechanism is supported through a runtime dynamic link to libgssapi_krb5. This must be installed separately, but is likely already installed on your system. If not you can install it by:

Debian-based systems

apt-get install libgssapi-krb5-2

RHEL-based systems

yum install krb5-libs

MacOS

brew install krb5

Supported HDFS Settings

The client will attempt to read Hadoop configs core-site.xml and hdfs-site.xml in the directories $HADOOP_CONF_DIR or if that doesn't exist, $HADOOP_HOME/etc/hadoop. Currently the supported configs that are used are:

  • fs.defaultFS - Client::default() support
  • dfs.ha.namenodes - name service support
  • dfs.namenode.rpc-address.* - name service support
  • dfs.client.failover.resolve-needed.* - DNS based NameNode discovery
  • dfs.client.failover.resolver.useFQDN.* - DNS based NameNode discovery
  • dfs.client.failover.random.order.* - Randomize order of NameNodes to try
  • fs.viewfs.mounttable.*.link.* - ViewFS links
  • fs.viewfs.mounttable.*.linkFallback - ViewFS link fallback

All other settings are generally assumed to be the defaults currently. For instance, security is assumed to be enabled and SASL negotiation is always done, but on insecure clusters this will just do SIMPLE authentication. Any setups that require other customized Hadoop client configs may not work correctly.

Building

cargo build

Object store implementation

An object_store implementation for HDFS is provided in the hdfs-native-object-store crate.

Running tests

The tests are mostly integration tests that utilize a small Java application in rust/mindifs/ that runs a custom MiniDFSCluster. To run the tests, you need to have Java, Maven, Hadoop binaries, and Kerberos tools available and on your path. Any Java version between 8 and 17 should work.

cargo test -p hdfs-native --features intergation-test

Python tests

See the Python README

Running benchmarks

Some of the benchmarks compare performance to the JVM based client through libhdfs via the fs-hdfs3 crate. Because of that, some extra setup is required to run the benchmarks:

export HADOOP_CONF_DIR=$(pwd)/rust/target/test
export CLASSPATH=$(hadoop classpath)

then you can run the benchmarks with

cargo bench -p hdfs-native --features benchmark

The benchmark feature is required to expose minidfs and the internal erasure coding functions to benchmark.

Running examples

The examples make use of the minidfs module to create a simple HDFS cluster to run the example. This requires including the integration-test feature to enable the minidfs module. Alternatively, if you want to run the example against an existing HDFS cluster you can exclude the integration-test feature and make sure your HADOOP_CONF_DIR points to a directory with HDFS configs for talking to your cluster.

cargo run --example simple --features integration-test