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What are the pros and cons of the Apache Parquet format compared to . . . "Overall, Parquet showed either similar or better results on every test [than Avro] The query-performance differences on the larger datasets in Parquet’s favor are partly due to the compression results; when querying the wide dataset, Spark had to read 3 5x less data for Parquet than Avro
Inspect Parquet from command line - Stack Overflow parquet-avro: Could not resolve dependencies for project org apache parquet:parquet-avro:jar:1 15 0-SNAPSHOT: org apache parquet:parquet-hadoop:jar: tests:1 15 0-SNAPSHOT was not found in https: jitpack io during a previous attempt This failure was cached in the local repository and resolution is not reattempted until the update interval of
Is it possible to read parquet files in chunks? - Stack Overflow If your parquet file was not created with row groups, the read_row_group method doesn't seem to work (there is only one group!) However if your parquet file is partitioned as a directory of parquet files you can use the fastparquet engine, which only works on individual files, to read files then, concatenate the files in pandas or get the
Is it better to have one large parquet file or lots of smaller parquet . . . The only downside of larger parquet files is it takes more memory to create them So you can watch out if you need to bump up Spark executors' memory row groups are a way for Parquet files to have vertical partitioning Each row group has many row chunks (one for each column, a way to provide horizontal partitioning for the datasets in parquet)
How to view Apache Parquet file in Windows? - Stack Overflow Apache Parquet is a binary file format that stores data in a columnar fashion Data inside a Parquet file is similar to an RDBMS style table where you have columns and rows But instead of accessing the data one row at a time, you typically access it one column at a time Apache Parquet is one of the modern big data storage formats
Methods for writing Parquet files using Python? - Stack Overflow Second, write the table into parquet file say file_name parquet # Parquet with Brotli compression pq write_table(table, 'file_name parquet') NOTE: parquet files can be further compressed while writing Following are the popular compression formats Snappy ( default, requires no argument) Gzip; Brotli; Parquet with Snappy compression
How to handle null values when writing to parquet from Spark So what are folks doing with regards to null column values today when writing out dataframe's to parquet? I can only think of very ugly horrible hacks like writing empty strings and well I have no idea what to do with numerical values to indicate null - short of putting some sentinel value in and having my code check for it (which is
Unable to infer schema when loading Parquet file By default the spark parquet source is using "partition inferring" which means it requires the file path to be partition in Key=Value pairs and the loads happens at the root To avoid this, if we assure all the leaf files have identical schema, then we can use df = spark read format("parquet")\ option("recursiveFileLookup", "true")
Python: save pandas data frame to parquet file - Stack Overflow import pyarrow as pa import pyarrow parquet as pq First, write the dataframe df into a pyarrow table # Convert DataFrame to Apache Arrow Table table = pa Table from_pandas(df_image_0) Second, write the table into parquet file say file_name parquet # Parquet with Brotli compression pq write_table(table, 'file_name parquet')
Spark parquet partitioning : Large number of files First I would really avoid using coalesce, as this is often pushed up further in the chain of transformation and may destroy the parallelism of your job (I asked about this issue here : Coalesce reduces parallelism of entire stage (spark))