parquet

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am currently using Cloudera 5.6 trying to create a parquet format table in hive table based off another table, but I am running into an error. create table sfdc_opportunities_sandbox_parquet like sfdc_opportunities_sandbox STORED AS PARQUET Error Message Parquet does not support date. See HIVE-6384 I read that hive 1.2 has a fix for this issue, but Cloudera 5.6 and 5.7 do not come with hive 1.2. Has anyone found way around this issue? 回答1: Except from using an other data type like TIMESTAMP or an other storage format like ORC , there might

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am reading text files and converting them to parquet files. I am doing it using spark code. But when i try to run the code I get following exception org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 1.0 failed 4 times, most recent failure: Lost task 2.3 in stage 1.0 (TID 9, ukfhpdbivp12.uk.experian.local): org.apache.spark.SparkException: Task failed while writing rows. at org.apache.spark.sql.sources.InsertIntoHadoopFsRelation.org$apache$spark$sql$sources$InsertIntoHadoopFsRelation$$writeRows$1(commands

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'm pretty new in Spark and I've been trying to convert a Dataframe to a parquet file in Spark but I haven't had success yet. The documentation says that I can use write.parquet function to create the file. However, when I run the script it shows me: AttributeError: 'RDD' object has no attribute 'write' from pyspark import SparkContext sc = SparkContext("local", "Protob Conversion to Parquet ") # spark is an existing SparkSession df = sc.textFile("/temp/proto_temp.csv") # Displays the content of the DataFrame to stdout df.write.parquet("

I get confusing messages when searching and reading answers on the internet on this subject. Anyone can share their experience? I know for a fact that gzipped csv is not, but maybe file internal structures for Parquet are such that it is totally different case for Parquet vs csv? Parquet files with GZIP compression are actually splittable. This is because of the internal layout of Parquet files. These are always splittable, independent of the used compression algorithm. This fact is mainly due to the design of Parquet files that divided in the following parts: Each Parquet files consists of

My team is building an ETL process to load raw delimited text files into a Parquet based "data lake" using Spark. One of the promises of the Parquet column store is that a query will only read the necessary "column stripes". But we're seeing unexpected columns being read for nested schema structures. To demonstrate, here is a POC using Scala and the Spark 2.0.1 shell: // Preliminary setup sc.setLogLevel("INFO") import org.apache.spark.sql.types._ import org.apache.spark.sql._ // Create a schema with nested complex structures val schema = StructType(Seq( StructField("F1", IntegerType),

response = "mi_or_chd_5" outcome = sqlc.sql("""select eid,{response} as response from outcomes where {response} IS NOT NULL""".format(response=response)) outcome.write.parquet(response, mode="overwrite") # Success print outcome.schema StructType(List(StructField(eid,IntegerType,true),StructField(response,ShortType,true))) But then: outcome2 = sqlc.read.parquet(response) # fail fails with: AnalysisException: u'Unable to infer schema for Parquet. It must be specified manually.;' in /usr/local/lib/python2.7/dist-packages/pyspark-2.1.0+hadoop2.7-py2.7.egg/pyspark/sql/utils.pyc in deco(*a, **kw)

I can read few json-files at the same time using * (star): sqlContext.jsonFile('/path/to/dir/*.json') Is there any way to do the same thing for parquet? Star doesn't works. See this issue on the spark jira. It is supported from 1.4 onwards. Without upgrading to 1.4, you could either point at the top level directory: sqlContext.parquetFile('/path/to/dir/') which will load all files in the directory. Alternatively, you could use the HDFS API to find the files you want, and pass them to parquetFile (it accepts varargs). FYI, you can also: read subset of parquet files using the wildcard symbol *