This function should work on all files, even if read_parquet() is
unable to read them, because of an unsupported schema, encoding,
compression or other reason.
Value
A named list with entries:
file_meta_data: a data frame with file meta data:file_name: file name.version: Parquet version, an integer.num_rows: total number of rows.key_value_metadata: list column of a data frames with two character columns calledkeyandvalue. This is the key-value metadata of the file. Arrow stores its schema here.created_by: A string scalar, usually the name of the software that created the file.
schema: data frame, the schema of the file. It has one row for each node (inner node or leaf node). For flat files this means one root node (inner node), always the first one, and then one row for each "real" column. For nested schemas, the rows are in depth-first search order. Most important columns are:file_name: file name.name: column name.type: data type. One of the low level data types.type_length: length for fixed length byte arrays.repettion_type: character, one ofREQUIRED,OPTIONALorREPEATED.logical_type: a list column, the logical types of the columns. An element has at least an entry calledtype, and potentially additional entries, e.g.bit_width,is_signed, etc.num_children: number of child nodes. Should be a non-negative integer for the root node, andNAfor a leaf node.
$row_groups: a data frame, information about the row groups.$column_chunks: a data frame, information about all column chunks, across all row groups. Some important columns:file_name: file name.row_group: which row group this chunk belongs to.column: which leaf column this chunks belongs to. The order is the same as in$schema, but only leaf columns (i.e. columns withNAchildren) are counted.file_path: which file the chunk is stored in.NAmeans the same file.file_offset: where the column chunk begins in the file.type: low level parquet data type.encodings: encodings used to store this chunk. It is a list column of character vectors of encoding names. Current possible encodings: "PLAIN", "GROUP_VAR_INT", "PLAIN_DICTIONARY", "RLE", "BIT_PACKED", "DELTA_BINARY_PACKED", "DELTA_LENGTH_BYTE_ARRAY", "DELTA_BYTE_ARRAY", "RLE_DICTIONARY", "BYTE_STREAM_SPLIT".path_in_scema: list column of character vectors. It is simply the path from the root node. It is simply the column name for flat schemas.codec: compression codec used for the column chunk. Possible values are: "UNCOMPRESSED", "SNAPPY", "GZIP", "LZO", "BROTLI", "LZ4", "ZSTD".num_values: number of values in this column chunk.total_uncompressed_size: total uncompressed size in bytes.total_compressed_size: total compressed size in bytes.data_page_offset: absolute position of the first data page of the column chunk in the file.index_page_offset: absolute position of the first index page of the column chunk in the file, orNAif there are no index pages.dictionary_page_offset: absolute position of the first dictionary page of the column chunk in the file, orNAif there are no dictionary pages.
See also
parquet_info() for a much shorter summary.
parquet_column_types() and parquet_schema() for column information.
read_parquet() to read, write_parquet() to write Parquet files,
nanoparquet-types for the R <-> Parquet type mappings.
Examples
file_name <- system.file("extdata/userdata1.parquet", package = "nanoparquet")
nanoparquet::parquet_metadata(file_name)
#> $file_meta_data
#> # A data frame: 1 × 5
#> file_name version num_rows key_value_metadata created_by
#> <chr> <int> <dbl> <I<list>> <chr>
#> 1 /home/runner/work/_temp/… 1 1000 <tbl [1 × 2]> https://g…
#>
#> $schema
#> # A data frame: 14 × 11
#> file_name name type type_length repetition_type converted_type
#> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 /home/runner/wo… sche… NA NA NA NA
#> 2 /home/runner/wo… regi… INT64 NA REQUIRED TIMESTAMP_MIC…
#> 3 /home/runner/wo… id INT32 NA REQUIRED INT_32
#> 4 /home/runner/wo… firs… BYTE… NA OPTIONAL UTF8
#> 5 /home/runner/wo… last… BYTE… NA REQUIRED UTF8
#> 6 /home/runner/wo… email BYTE… NA OPTIONAL UTF8
#> 7 /home/runner/wo… gend… BYTE… NA OPTIONAL UTF8
#> 8 /home/runner/wo… ip_a… BYTE… NA REQUIRED UTF8
#> 9 /home/runner/wo… cc BYTE… NA OPTIONAL UTF8
#> 10 /home/runner/wo… coun… BYTE… NA REQUIRED UTF8
#> 11 /home/runner/wo… birt… INT32 NA OPTIONAL DATE
#> 12 /home/runner/wo… sala… DOUB… NA OPTIONAL NA
#> 13 /home/runner/wo… title BYTE… NA OPTIONAL UTF8
#> 14 /home/runner/wo… comm… BYTE… NA OPTIONAL UTF8
#> # ℹ 5 more variables: logical_type <I<list>>, num_children <int>,
#> # scale <int>, precision <int>, field_id <int>
#>
#> $row_groups
#> # A data frame: 1 × 7
#> file_name id total_byte_size num_rows file_offset
#> <chr> <int> <dbl> <dbl> <dbl>
#> 1 /home/runner/work/_temp/Libr… 0 71427 1000 NA
#> # ℹ 2 more variables: total_compressed_size <dbl>, ordinal <int>
#>
#> $column_chunks
#> # A data frame: 13 × 19
#> file_name row_group column file_path file_offset offset_index_offset
#> <chr> <int> <int> <chr> <dbl> <dbl>
#> 1 /home/runne… 0 0 NA 4 NA
#> 2 /home/runne… 0 1 NA 6741 NA
#> 3 /home/runne… 0 2 NA 12259 NA
#> 4 /home/runne… 0 3 NA 15211 NA
#> 5 /home/runne… 0 4 NA 16239 NA
#> 6 /home/runne… 0 5 NA 31759 NA
#> 7 /home/runne… 0 6 NA 32031 NA
#> 8 /home/runne… 0 7 NA 42952 NA
#> 9 /home/runne… 0 8 NA 55009 NA
#> 10 /home/runne… 0 9 NA 55925 NA
#> 11 /home/runne… 0 10 NA 59312 NA
#> 12 /home/runne… 0 11 NA 67026 NA
#> 13 /home/runne… 0 12 NA 71089 NA
#> # ℹ 13 more variables: offset_index_length <int>,
#> # column_index_offset <dbl>, column_index_length <int>, type <chr>,
#> # encodings <I<list>>, path_in_schema <I<list>>, codec <chr>,
#> # num_values <dbl>, total_uncompressed_size <dbl>,
#> # total_compressed_size <dbl>, data_page_offset <dbl>,
#> # index_page_offset <dbl>, dictionary_page_offset <dbl>
#>