Do you know, can ParquetWriter or AvroParquetWriter store the schema separately without data?
Now schema is written into parquet file:
AvroParquetWriter.Builder builder = AvroParquetWriter.<GenericRecord>builder(new Path(file.getName()))
.withSchema(payload.getSchema())
.build90;
Do you know is possible write only data without schema into parquet file?
Thank you!
#ЭльфияВалиева. No, the parquet metadata (schema) in the footer is necessary to provide parquet readers the necessary schema to read the parquet data.
Related
I am writing data into parquet files programatically with AvroParquetWriter but i also want to write parquet file with bucketing, is it possible to do same with bucketing ?
Thanks in advance!!
I am trying to analyze twitter data using flume
i got the files from twitter using flume in BigInsights
but the data I received is of compressed Avro schema which is not readable
can anyone tell me a way so that can convert that file to JSON (Readable)
in order to do some analysis on it.
Or is there any way so that the data I receive is already in JSON (Readable) format.
Thanks In Advance.
This is the data i received
Avro format is not designed to be human readable and it's desinged to be consumed by programs. But you have a few options to view this data or even better analyze the data.
Create Hive Table: This option will allow you to analyze data using SQL queries, Spark SQL, Spark notebooks, visualization tools like Tableau and Excel too.
Your table creation script will look like this:
CREATE TABLE twitter_data
ROW FORMAT
SERDE 'org.apache.hadoop.hive.serde2.avro.AvroSerDe'
STORED AS
INPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerOutputFormat'
TBLPROPERTIES ('avro.schema.literal'='{...
In schema literal, you can define your own schema too.
Write Program: If you are developer and want to/like to wrangle data using programming, you have many languages to choose from to read, parse, convert and write from Avro file to JSON.
I need to move my data from a relational database to HDFS but i would like to save the data to a parquet-avro file format. Looking at the sqoop documentation it seems like my options are --as-parquetfile or --as-avrodatafile, but not a mix of both. From my understanding of this blog/picture below, the way parquet-avro works is that it is a parquet file with the avro schema embedded and a converter to convert and save an avro object to a parquet file and vise versa.
My initial assumption is that if i use the sqoop option --as-parquetfile then the data being saved to the parquet file will be missing the avro schema and the converter won't work. However upon looking at the sqoop code that saves the data to a parquet file format it does seem to be using a util related to avro but i'm not sure what's going on. Could someone clarify? If i cannot do this with sqoop, what other options do i have?
parquet-avro is mainly a convenience layer so that you can read/write data that is stored in Apache Parquet into Avro object. When you read the Parquet again with parquet-avro, the Avro schema is inferred from the Parquet schema (alternatively you should be able to specify an explicit Avro schema). Thus you should be fine with --as-parquetfile.
I have to build a tool which will process our data storage from HBase(HFiles) to HDFS in parquet format.
Please suggest one of the best way to move data from HBase tables to Parquet tables.
We have to move 400 million records from HBase to Parquet. How to achieve this and what is the fastest way to move data?
Thanks in advance.
Regards,
Pardeep Sharma.
Please have a look in to this project tmalaska/HBase-ToHDFS
which reads a HBase table and writes the out as Text, Seq, Avro, or Parquet
Example usage for parquet :
Exports the data to Parquet
hadoop jar HBaseToHDFS.jar ExportHBaseTableToParquet exportTest c export.parquet false avro.schema
I recently opensourced a patch to HBase which tackles the problem you are describing.
Have a look here: https://github.com/ibm-research-ireland/hbaquet
when querying a table, a SerDe will deserialize a row of data from the bytes in the file to objects used internally by Hive to operate on that row of data. when performing an INSERT or CTAS (see “Importing Data” on page 441), the table’s SerDe will serialize Hive’s internal representation of a row of data into the bytes that are written to the output file.
Is serDe library?
How does hive store data i.e it stores in file or table?
Please can anyone explain the bold sentences clearly?
I'm new to hive!!
Answers
Yes, SerDe is a Library which is built-in to the Hadoop API
Hive uses Files systems like HDFS or any other storage (FTP) to store data, data here is in the form of tables (which has rows and columns).
SerDe - Serializer, Deserializer instructs hive on how to process a record (Row). Hive enables semi-structured (XML, Email, etc) or unstructured records (Audio, Video, etc) to be processed also. For Example If you have 1000 GB worth of RSS Feeds (RSS XMLs). You can ingest those to a location in HDFS. You would need to write a custom SerDe based on your XML structure so that Hive knows how to load XML files to Hive tables or other way around.
For more information on how to write a SerDe read this post
In this aspect we can see Hive as some kind of database engine. This engine is working on tables which are built from records.
When we let Hive (as well as any other database) to work in its own internal formats - we do not care.
When we want Hive to process our own files as tables (external tables) we have to let him know - how to translate data in files into records. This is exactly the role of SerDe. You can see it as plug-in which enables Hive to read / write your data.
For example - you want to work with CSV. Here is example of CSV_Serde
https://github.com/ogrodnek/csv-serde/blob/master/src/main/java/com/bizo/hive/serde/csv/CSVSerde.java
Method serialize will read the data, and chop it into fields assuming it is CSV
Method deserialize will take a record and format it as CSV.
Hive can analyse semi structured and unstructured data as well by using
(1) complex data type(struct,array,unions)
(2) By using SerDe
SerDe interface allow us to instruct hive as to how the record should be processed. Serializer will take java object that hive has been working on,and convert it into something that hive can store and Deserializer take binary representation of a record and translate into java object that hive can manipulate.
I think the above has the concepts serialise and deserialise back to front. Serialise is done on write, the structured data is serialised into a bit/byte stream for storage. On read, the data is deserialised from the bit/byte storage format to the structure required by the reader. eg Hive needs structures that look like rows and columns but hdfs stores the data in bit/byte blocks, so serialise on write, deserialise on read.