On Apache's official website, this is the official explanation of this parameter:
When true, the Parquet data source merges schemas collected from all data files, otherwise the schema is picked from the summary file or a random data file if no summary file is available.
In fact, my question is, what is the summary file?
Apache Parquet uses metadata to store all information required to load the data from a file, like column metadata, dictionaries row groups and so on.
The format is designed to keep this metadata embeded in the file itself, or stored a separate file. This is what summary file is.
Parquet summary file contains a collection of footers from actual Parquet data files in a directory. It can be used to skip RowGroups when reading w/o fetching the footer from each individual Parquet file which may be expensive if you have a lot of files and/or on Blob stores.
https://github.com/apache/parquet-mr/blob/65b95fb72be8f5a8a193a6f7bc4560fdcd742fc7/parquet-hadoop/src/main/java/org/apache/parquet/hadoop/ParquetFileReader.java#L407
Parquet storage format is the columnar oriented file format, that means data for a particular column for all the rows will be stored adjacent to each other, which results in two main benefits - better compression ratio and increased query performance.
Related
For a project I need to append frequently but on a non-periodic way about one thousand or more data files (tabular data) on one existing CSV or parquet file with same schema in Hadoop/HDFS (master=yarn). At the end, I need to be able to do some filtering on the result file - to extract subset of data.
One dummy file may look like this (very simple example):
id,uuid,price
1,16c533c3-c191-470c-97d9-e1e01ccc3080,46159
2,6bb0917b-2414-4b24-85ca-ae2c2713c9a0,50222
3,7b1fa3f9-2db2-4d93-a09d-ca6609cfc834,74591
4,e3a3f874-380f-4c89-8b3e-635296a70d76,91026
5,616dd6e8-5d05-4b07-b8f2-7197b579a058,73425
6,23e77a21-702d-4c87-a69c-b7ace0626616,34874
7,339e9a7f-efb1-4183-ac32-d365e89537bb,63317
8,fee09e5f-6e16-4d4f-abd1-ecedb1b6829c,6642
9,2e344444-35ee-47d9-a06a-5a8bc01d9eab,55931
10,d5cba8d6-f0e1-49c8-88e9-2cd62cde9737,51792
Number of rows may vary between 10 and about 100000
On user request, all input files copied in a source folder should be ingested by an ETL pipeline and appended at the end of one single CSV/parquet file or any other appropriate file format (no DB). Data from a single input file may be spread over one, two or more partitions.
Because the input data files may all have different number of rows, I am concerned about getting partitions with different sizes in the resulting CSV/parquet file. Sometimes all the data may be append in one new file. Sometimes the data is so big that several files are appended.
And because input files may be appended a lot of time from different users and different sources, I am also concerned that the result CSV/parquet may contains too much part-files for the namenode to handle them.
I have done some small test appending data on existing CSV / parquet files and noticed that for each appending, a new file was generated - for example:
df.write.mode('append').csv('/user/applepy/pyspark_partition/uuid.csv')
will append the new data as a new file in the file 'uuid.csv' (which is actually a directory generated by pyspark containing all pieces of appended data).
Doing some load tests based on real conditions, I quickly realized that I was generating A LOT of files (several 10-thousands). At some point I got so much files that PySpark was unable to simple count the number of rows (NameNode memory overflow).
So I wonder how to solve this problem. What would be the best practice here? Read the whole file, append the data chunk, same the data in a new file doesn't seems to be very efficient here.
NameNode memory overflow
Then increase the heapsize of the namenode
quickly realized that I was generating A LOT of files
HDFS write operations almost never append to single files. They append "into a directory", and create new files, yes.
From Spark, you can use coalesce and repartition to create larger writer batches.
As you'd mentioned, you wanted parquet, so write that then. That'll cause you to have even smaller file sizes in HDFS.
or any other appropriate file format (no DB)
HDFS is not really the appropriate tool for this. Clickhouse, Druid, and Pinot are the current real time ingest / ETL tools being used, especially when data is streamed in "non periodically" from Kafka
I would like to check how each column of parquet data contributes to total file size / total table size.
I looked through Spark/Databricks commands, parquet-cli, parquet-tools and unfortunately it seems that none of them provide such information directly. Considering that this is a columnar format, it should be possible to pull out somehow.
So far the closest I got would be to run parquet-tools meta, summing up details by column for each row group within the file, then aggregating this for all files of a table. This means iterating on all parquet files and cumbersome parsing of the output.
Maybe there is an easier way?
Your approach is correct. Here is a py script using DuckDB to find overall compressed and uncompressed size of all the columns in a parquet dataset.
import duckdb
con = duckdb.connect(database=':memory:')
print(con.execute("""SELECT SUM(total_compressed_size) AS
total_compressed_size_in_bytes, SUM(total_uncompressed_size) AS
total_uncompressed_size_in_bytes, path_in_schema AS column_name from
parquet_metadata('D:\\dev\\tmp\\parq_dataset\\*') GROUP BY path_in_schema""").df())
D:\\dev\\tmp\\parq_dataset\\* here parq_dataset consists of multiple parquet files with same schema. Something similar should be possible using other libraries like pyarrow/fastparquet as well.
I'm exploring ways to store a high volume of data from sensors (time series data), in a way that's scalable and cost-effective.
Currently, I'm writing a CSV file for each sensor, partitioned by date, so my filesystem hierarchy looks like this:
client_id/sensor_id/year/month/day.csv
My goal is to be able to perform SQL queries on this data, (typically fetching time ranges for a specific client/sensor, performing aggregations, etc) I've tried loading it to Postgres and timescaledb, but the volume is just too large and the queries are unreasonably slow.
I am now experimenting with using Spark and Parquet files to perform these queries, but I have some questions I haven't been able to answer from my research on this topic, namely:
I am converting this data to parquet files, so I now have something like this:
client_id/sensor_id/year/month/day.parquet
But my concern is that when Spark loads the top folder containing the many Parquet files, the metadata for the rowgroup information is not as optimized as if I used one single parquet file containing all the data, partitioned by client/sensor/year/month/day. Is this true? Or is it the same to have many parquet files or a single partitioned Parquet file? I know that internally the parquet file is stored in a folder hierarchy like the one I am using, but I'm not clear on how that affects the metadata for the file.
The reason I am not able to do this is that I am continuously receiving new data, and from my understanding, I cannot append to a parquet file due to the nature that the footer metadata works. Is this correct? Right now, I simply convert the previous day's data to parquet and create a new file for each sensor of each client.
Thank you.
You can use Structured Streaming with kafka(as you are already using it) for real time processing of your data and store data in parquet format. And, yes you can append data to parquet files. Use SaveMode.Append for that such as
df.write.mode('append').parquet(path)
You can even partition your data on hourly basis.
client/sensor/year/month/day/hour which will further provide you performance improvement while querying.
You can create hour partition based on system time or timestamp column based on type of query you want to run on your data.
You can use watermaking for handling late records if you choose to partition based on timestamp column.
Hope this helps!
I could share my experience and technology stack that being used at AppsFlyer.
We have a lot of data, about 70 billion events per day.
Our time-series data for near-real-time analytics are stored in Druid and Clickhouse. Clickhouse is used to hold real-time data for the last two days; Druid (0.9) wasn't able to manage it. Druid holds the rest of our data, which populated daily via Hadoop.
Druid is a right candidate in case you don't need a row data but pre-aggregated one, on a daily or hourly basis.
I would suggest you let a chance to the Clickhouse, it lacks documentation and examples but works robust and fast.
Also, you might take a look at Apache Hudi.
We are getting varieties of JSONs/XMLs as input where schema is always evolving. I want to process them using ORC or Parquet format in Hadoop/Hive environment for performance gain.
I know below common style of achieving same objective :
Use JSONSerde or XMLSerde library, first create hive table using these serde. Later select * fields query will be fired on each xml/json hive table to save as orc or save as parquet into another table. Once done successful I can drop these Serde Table and XML/JSON data.
What would be another good ways of doing same ?
As suggested by you, this is the most common way to do an offline conversion of JSON/XML data to parquet format.
But another way could be to parse the JSON/XML and create Parquet Groups for each of the JSON records. Essentially:
Open the JSON file
Read each individual record
Open another file
Create a Parquet Group from the record read in #2
Write the parquet group to the file created in #3
Do this for all records in the file
Close both files.
We came up with such a converter for one of our used case.
I have a quite hefty parquet file where I need to change values for one of the column. One way to do this would be to update those values in source text files and recreate parquet file but I'm wondering if there is less expensive and overall easier solution to this.
Lets start with basics:
Parquet is a file format that needs to be saved in a file system.
Key questions:
Does parquet support append operations?
Does the file system (namely, HDFS) allow append on files?
Can the job framework (Spark) implement append operations?
Answers:
parquet.hadoop.ParquetFileWriter only supports CREATE and OVERWRITE; there is no append mode. (Not sure but this could potentially change in other implementations -- parquet design does support append)
HDFS allows append on files using the dfs.support.append property
Spark framework does not support append to existing parquet files, and with no plans to; see this JIRA
It is not a good idea to append to an existing file in distributed systems, especially given we might have two writers at the same time.
More details are here:
http://bytepadding.com/big-data/spark/read-write-parquet-files-using-spark/
http://bytepadding.com/linux/understanding-basics-of-filesystem/
There are workarounds, but you need to create your parquet file in a certain way to make it easier to update.
Best practices:
A. Use row groups to create parquet files. You need to optimize how many rows of data can go into a row group before features like data compression and dictionary encoding stop kicking in.
B. Scan row groups one at a time and figure out which row groups need to be updated. Generate new parquet files with amended data for each modified row group. It is more memory efficient to work with one row group's worth of data at a time instead of everything in the file.
C. Rebuild the original parquet file by appending unmodified row groups and with modified row groups generated by reading in one parquet file per row group.
it's surprisingly fast to reassemble a parquet file using row groups.
In theory it should be easy to append to existing parquet file if you just strip the footer (stats info), append new row groups and add new footer with update stats, but there isn't an API / Library that supports it..
Look at this nice blog which can answer your question and provide a method to perform updates using Spark(Scala):
http://aseigneurin.github.io/2017/03/14/incrementally-loaded-parquet-files.html
Copy & Paste from the blog:
when we need to edit the data, in our data structures (Parquet), that are immutable.
You can add partitions to Parquet files, but you can’t edit the data in place.
But ultimately we can mutate the data, we just need to accept that we won’t be doing it in place. We will need to recreate the Parquet files using a combination of schemas and UDFs to correct the bad data.
If you want to incrementally append the data in Parquet (you did n't ask this question, still it would be useful for other readers)
Refer this well written blog:
http://aseigneurin.github.io/2017/03/14/incrementally-loaded-parquet-files.html
Disclaimer: I have n't written those blogs, I just read it and found it might be useful for others.
You must re-create the file, this is the Hadoop way. Especially if the file is compressed.
Another approach, (very common in Big-data), is to do the update on another Parquet (or ORC) file, then JOIN / UNION at query time.
Well, in 2022, I strongly recommend to use a lake house solution, like deltaLake or Apache Iceberg. They will care about that for you.