parquet

问题 The data I have is a kind of streaming data. And I want to store them into a single Parquet file. But Pyarrow will overwrite the Parquet file everytime. So How should I do? I try not to close the writer but it seems impossible since If I didn't close it, then I could not read this file. Here is the package: import pyarrow.parquet as pp import pyarrow as pa for name in ['LEE','LSY','asd','wer']: writer=pq.ParquetWriter('d:/test.parquet', table.schema) arrays=[pa.array([name]),pa.array([2])]

问题 Has anybody tried reading/writing Parquet file using Apache Beam. Support is added recently in version 2.5.0, hence not much documentation. I am trying to read json input file and would like to write to parquet format. Thanks in advance. 回答1: You will need to use ParquetIO.Sink. It implements FileIO. 回答2: Add the following dependency as ParquetIO in different module. <dependency> <groupId>org.apache.beam</groupId>; <artifactId>beam-sdks-java-io-parquet</artifactId>; <version>2.6.0</version>;

问题 Has anybody tried reading/writing Parquet file using Apache Beam. Support is added recently in version 2.5.0, hence not much documentation. I am trying to read json input file and would like to write to parquet format. Thanks in advance. 回答1: You will need to use ParquetIO.Sink. It implements FileIO. 回答2: Add the following dependency as ParquetIO in different module. <dependency> <groupId>org.apache.beam</groupId>; <artifactId>beam-sdks-java-io-parquet</artifactId>; <version>2.6.0</version>;

问题 We have data stored in s3 partitioned in the following structure: bucket/directory/table/aaaa/bb/cc/dd/ where aaaa is the year, bb is the month, cc is the day and dd is the hour. As you can see, there are no partition keys in the path ( year=aaaa , month=bb , day=cc , hour=dd) . As a result, when I read the table into Spark, there is no year , month , day or hour columns. Is there anyway I can read the table into Spark and include the partitioned column without : changing the path names in s3

问题 I have searched through every documentation and still didn't find why there is a prefix and what is c000 in the below file naming convention: file:/Users/stephen/p/spark/f1/part-00000-445036f9-7a40-4333-8405-8451faa44319- c000.snappy.parquet 回答1: You should use "Talk is cheap, show me the code." methodology. Everything is not documented and one way to go is just the code. Consider part-1-2_3-4.parquet : Split/Partition number. Random UUID to prevent collision between different (appending)

问题 I have searched through every documentation and still didn't find why there is a prefix and what is c000 in the below file naming convention: file:/Users/stephen/p/spark/f1/part-00000-445036f9-7a40-4333-8405-8451faa44319- c000.snappy.parquet 回答1: You should use "Talk is cheap, show me the code." methodology. Everything is not documented and one way to go is just the code. Consider part-1-2_3-4.parquet : Split/Partition number. Random UUID to prevent collision between different (appending)

问题 I am working on a utility which reads multiple parquet files at a time and writing them into one single output file. the implementation is very straightforward. This utility reads parquet files from the directory, reads Group from all the file and put them into a list .Then uses ParquetWrite to write all these Groups into a single file. After reading 600mb it throws Out of memory error for Java heap space. It also takes 15-20 minutes to read and write 500mb of data. Is there a way to make

问题 You can use S3 Select with Spark on Amazon EMR and with Databricks, but only for CSV and JSON files. I am guessing that S3 Select isn't offered for columnar file formats because it wouldn't help that much. Let's say we have a data lake of people with first_name , last_name and country columns. If the data is stored as CSV files and you run a query like peopleDF.select("first_name").distinct().count() , then S3 will transfer all the data for all the columns to the ec2 cluster to run the

问题 You can use S3 Select with Spark on Amazon EMR and with Databricks, but only for CSV and JSON files. I am guessing that S3 Select isn't offered for columnar file formats because it wouldn't help that much. Let's say we have a data lake of people with first_name , last_name and country columns. If the data is stored as CSV files and you run a query like peopleDF.select("first_name").distinct().count() , then S3 will transfer all the data for all the columns to the ec2 cluster to run the

问题 I have a scenario in my project , where I am reading the kafka topic messages using spark-sql-2.4.1 version. I am able to process the day using structured streaming. Once the data is received and after processed I need to save the data into respective parquet files in hdfs store. I am able to store and read parquet files, I kept a trigger time of 15 seconds to 1 minutes. These files are very small in size hence resulting into many files. These parquet files need to be read latter by hive