github.com/fraugster/parquet-go (go): Package goparquet is an implementation of the parquet file format in Go. It provides functionality to both read and write parquet files, as well as high-level functionality to manage the data schema of parquet files, to directly write Go objects to parquet files using automatic or custom marshalling and to read records from parquet files into Go objects using automatic or custom marshalling.  parquet is a file format to store nested data structures in a flat columnar format. By storing in a column-oriented way, it allows for efficient reading of individual columns without having to read and decode complete rows. This allows for efficient reading and faster processing when using the file format in conjunction with distributed data processing frameworks like Apache Hadoop or distributed SQL query engines like Presto and AWS Athena.  This particular implementation is divided into several packages. The top-level package that you're currently viewing is the low-level implementation of the file format. It is accompanied by the sub-packages parquetschema and floor.  parquetschema provides functionality to parse textual schema definitions as well as the data types to manually or programmatically construct schema definitions by other means that are open to the user. The textual schema definition format is based on the barely documented schema definition format that is implemented in the parquet Java implementation. See the parquetschema sub-package for further documentation on how to use this package and the grammar of the schema definition format as well as examples.  floor is a high-level wrapper around the low-level package. It provides functionality to open parquet files to read from them or to write to them. When reading from parquet files, floor takes care of automatically unmarshal the low-level data into the user-provided Go object. When writing to parquet files, user-provided Go objects are first marshalled to a low-level data structure that is then written to the parquet file. These mechanisms allow to directly read and write Go objects without having to deal with the details of the low-level parquet format. Alternatively, marshalling and unmarshalling can be implemented in a custom manner, giving the user maximum flexibility in case of disparities between the parquet schema definition and the actual Go data structure. For more information, please refer to the floor sub-package's documentation.  To aid in working with parquet files, this package also provides a commandline tool named "parquet-tool" that allows you to inspect a parquet file's schema, meta data, row count and content as well as to merge and split parquet files.  When operating with parquet files, most users should be able to cover their regular use cases of reading and writing files using just the high-level floor package as well as the parquetschema package. Only if a user has more special requirements in how to work with the parquet files, it is advisable to use this low-level package.  To write to a parquet file, the type provided by this package is the FileWriter. Create a new *FileWriter object using the NewFileWriter function. You have a number of options available with which you can influence the FileWriter's behaviour. You can use these options to e.g. set meta data, the compression algorithm to use, the schema definition to use, or whether the data should be written in the V2 format. If you didn't set a schema definition, you then need to manually create columns using the functions NewDataColumn, NewListColumn and NewMapColumn, and then add them to the FileWriter by using the AddColumn method. To further structure your data into groups, use AddGroup to create groups. When you add columns to groups, you need to provide the full column name using dotted notation (e.g. "groupname.fieldname") to AddColumn. Using the AddData method, you can then add records. The provided data is of type map[string]interface{}. This data can be nested: to provide data for a repeated field, the data type to use for the map value is []interface{}. When the provided data is a group, the data type for the group itself again needs to be map[string]interface{}.  The data within a parquet file is divided into row groups of a certain size. You can either set the desired row group size as a FileWriterOption, or you can manually check the estimated data size of the current row group using the CurrentRowGroupSize method, and use FlushRowGroup to write the data to disk and start a new row group. Please note that CurrentRowGroupSize only estimates the _uncompressed_ data size. If you've enabled compression, it is impossible to predict the compressed data size, so the actual row groups written to disk may be a lot smaller than uncompressed, depending on how efficiently your data can be compressed.  When you're done writing, always use the Close method to flush any remaining data and to write the file's footer.  To read from files, create a FileReader object using the NewFileReader function. You can optionally provide a list of columns to read. If these are set, only these columns are read from the file, while all other columns are ignored. If no columns are proided, then all columns are read.  With the FileReader, you can then go through the row groups (using PreLoad and SkipRowGroup). and iterate through the row data in each row group (using NextRow). To find out how many rows to expect in total and per row group, use the NumRows and RowGroupNumRows methods. The number of row groups can be determined using the RowGroupCount method.
Keywords: athena|golang|golang-package|hacktoberfest|hadoop|parquet|parquet-schema|presto