There are lots of tradeoffs and factors to consider here. Splunk Analytics for Hadoop will work with many popular compression codecs utilizing commons-compress-1.10.jar (Splunk 6.6.2)
Here is a list of codecs:
bzip2, gzip, pack200, lzma, xz, Snappy, traditional Unix Compress, DEFLATE, LZ4, Brotli and ar, cpio, jar, tar, zip, dump, 7z, arj Commons Compress 1.10 On Maven
With that said, I'd suggest you consult with your Hadoop vendor or experiment to see what gives you the best performance for the given compression ratio. One recommendation I am comfortable giving would be to pick a compression codec that is natively splittable in Hadoop like bzip2, Snappy or LZO. I've seen performance issues on non-splittable compression codecs like gzip.
Since Hunk submit the job to Hadoop for processing the same Hadoop recommendation you get from Cloudera, Hortonworks, or MapR also applies here.
As you highlighted, there is a tradeoff between speed and compression rate, and Snappy seems like the favorite codec.
However, before you select the compression codec, I highly recommend you select the right File format first (Text, Avro, Parquet, ORC) and only then decide about the Codec (note that not all formats support all compression options): https://www.slideshare.net/oom65/file-format-benchmarks-avro-json-orc-parquet