Github user liancheng commented on the pull request:
https://github.com/apache/spark/pull/9060#issuecomment-156077334
You may construct a Parquet file consists of a single column with
dictionary encoding using:
```scala
val path = "file:///tmp/parquet/dict"
sqlContext.range(1 << 16).selectExpr("(id % 4) AS
i").coalesce(1).write.mode("overwrite").parquet(path)
```
And here are instructions of building and installing the parquet-tools CLI
tool. Then you can inspect Parquet metadata using:
```
$ parquet-meta /tmp/parquet/dict
file:
file:/private/tmp/parquet/dict/part-r-00000-88498608-9eed-4728-b96a-b60bc5ebc2a8.gz.parquet
creator: parquet-mr version 1.6.0
extra: org.apache.spark.sql.parquet.row.metadata =
{"type":"struct","fields":[{"name":"i","type":"long","nullable":true,"metadata":{}}]}
file schema: root
----------------------------------------------------------------------------------------------------------------------------------------------
i: OPTIONAL INT64 R:0 D:1
row group 1: RC:65536 TS:16615 OFFSET:4
----------------------------------------------------------------------------------------------------------------------------------------------
i: INT64 GZIP DO:0 FPO:4 SZ:198/16615/83.91 VC:65536
ENC:BIT_PACKED,RLE,PLAIN_DICTIONARY
```
The `ENC:...` part in the last line is column encoding information.
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