parquet | 易学教程

Force dask to_parquet to write single file

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问题 When using dask.to_parquet(df, filename) a subfolder filename is created and several files are written to that folder, whereas pandas.to_parquet(df, filename) writes exactly one file. Can I use dask's to_parquet (without using compute() to create a pandas df) to just write a single file? 回答1: Writing to a single file is very hard within a parallelism system. Sorry, such an option is not offered by Dask (nor probably any other parallel processing library). You could in theory perform the

Force dask to_parquet to write single file

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Read parquet data from ByteArrayOutputStream instead of file

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问题 I would like to convert this code: import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.parquet.column.page.PageReadStore; import org.apache.parquet.example.data.simple.SimpleGroup; import org.apache.parquet.example.data.simple.convert.GroupRecordConverter; import org.apache.parquet.hadoop.ParquetFileReader; import org.apache.parquet.hadoop.util.HadoopInputFile; import org.apache.parquet.io.ColumnIOFactory; import org.apache.parquet.io

Read parquet data from ByteArrayOutputStream instead of file

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Read parquet data from ByteArrayOutputStream instead of file

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hive table gives error Unimplemented type

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问题 Using spark-sql-2.4.1, and writing a parquet file with schema containing |-- avg: double (nullable = true) While reading the same using val df = spark.read.format("parquet").load(); Getting error: UnsupportedOperationException: Unimplemented type: DoubleType. So what is wrong here, and how to fix this? Stack Trace: Caused by: java.lang.UnsupportedOperationException: Unimplemented type: DoubleType at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readIntBatch

Pandas to parquet NOT into file-system but get content of resulting file in variable

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问题 There are several ways how a conversion from pandas to parquet is possible. e.g. pyarrow.Table.from_pandas or dataframe.to_parquet . What they have in common is that they get as a parameter a filePath where the df.parquet should be stored. I need to get the content of the written parquet file into a variable and have not seen this, yet. Mainly I want the same behavior as pandas.to_csv which returns the result as a string if no path is provided. Of course I could just write the file and read

PySpark Write Parquet Binary Column with Stats (signed-min-max.enabled)

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问题 I found this apache-parquet ticket https://issues.apache.org/jira/browse/PARQUET-686 which is marked as resolved for parquet-mr 1.8.2. The feature I want is the calculated min/max in the parquet metadata for a ( string or BINARY ) column. And referencing this is an email https://lists.apache.org/thread.html/%3CCANPCBc2UPm+oZFfP9oT8gPKh_v0_BF0jVEuf=Q3d-5=ugxSFbQ@mail.gmail.com%3E which uses scala instead of pyspark as an example: Configuration conf = new Configuration(); + conf.set("parquet

Transfer and write Parquet with python and pandas got timestamp error

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问题 I tried to concat() two parquet file with pandas in python . It can work , but when I try to write and save the Data frame to a parquet file ,it display the error : ArrowInvalid: Casting from timestamp[ns] to timestamp[ms] would lose data: I checked the doc. of pandas, it default the timestamp syntax in ms when write the parquet file. How can I white the parquet file with used schema after concat? Here is my code: import pandas as pd table1 = pd.read_parquet(path= ('path.parquet'),engine=

Flink 新场景：OLAP 引擎性能优化及应用案例

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摘要：本文由阿里巴巴技术专家贺小令（晓令）分享，主要介绍 Apache Flink 新场景 OLAP 引擎，内容分为以下四部分：背景介绍 Flink OLAP 引擎案例介绍未来计划一、背景介绍 1.OLAP 及其分类 OLAP 是一种让用户可以用从不同视角方便快捷的分析数据的计算方法。主流的 OLAP 可以分为3类：多维 OLAP ( Multi-dimensional OLAP )、关系型 OLAP ( Relational OLAP ) 和混合 OLAP ( Hybrid OLAP ) 三大类。（1）多维 OLAP ( MOLAP ) 传统的 OLAP 分析方式数据存储在多维数据集中（2）关系型 OLAP ( ROLAP ) 以关系数据库为核心，以关系型结构进行多维数据的表示通过 SQL 的 where 条件以呈现传统 OLAP 的切片、切块功能（3）混合 OLAP ( HOLAP ) 将 MOLAP 和 ROLPA 的优势结合起来，以获得更快的性能以下将详细介绍每种分类的具体特征。 ■ 多维 OLAP ( MOLAP ) MOLAP 的典型代表是 Kylin 和 Druid。 MOLAP 处理流程首先，对原始数据做数据预处理；然后，将预处理后的数据存至数据仓库，用户的请求通过 OLAP server 即可查询数据仓库中的数据。 MOLAP 的优点和缺点