{"push_id":1005437566,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"aa32be0698be14a3f9ed24f101ddb00c0896c479","before":"edf9979c9a03b680390620cb4c367be7232f7875","commits":[{"sha":"aa32be0698be14a3f9ed24f101ddb00c0896c479","author":{"email":"2680965ad5aae29275c8fb10150cb10f5554e78b@gmail.com","name":"hyukjinkwon"},"message":"Support for specifying custom date format for date and timestamp types.\n\nhttps://github.com/databricks/spark-csv/issues/279\nhttps://github.com/databricks/spark-csv/issues/262\nhttps://github.com/databricks/spark-csv/issues/266\n\nThis PR adds the support to specify custom date format for `DateType` and `TimestampType`.\n\nFor `TimestampType`, this uses the given format to infer schema and also to convert the values\nFor `DateType`, this uses the given format to convert the values.\nIf the `dateFormat` is not given, then it works with `Timestamp.valueOf()` and `Date.valueOf()` for backwords compatibility.\nWhen it's given, then it uses `SimpleDateFormat` for parsing data.\n\nIn addition, `IntegerType`, `DoubleType` and `LongType` have a higher priority than `TimestampType` in type inference. This means even if the given format is `yyyy` or `yyyy.MM`, it will be inferred as `IntegerType` or `DoubleType`. Since it is type inference, I think it is okay to give such precedences.\n\nAuthor: hyukjinkwon <gurwls223@gmail.com>\n\nCloses #280 from HyukjinKwon/ISSUE-266-timestamp.","distinct":true,"url":"https://api.github.com/repos/falaki/spark-csv/commits/aa32be0698be14a3f9ed24f101ddb00c0896c479"}]}