Skip to main content

Which one should I use - PrestoDB or Trino ?

By Dinesh

 

First thing to understand is why to use Presto or Trino. 

  • We had been running two clusters specifically Hortonworks (HDP) variant & Cloudera (CDP) variant. 
  • Hive Tables built on HDP were mostly ORC whereas Tables that existed for us on CDP were mostly Parquet.
  • We wanted to add ad-hoc querying functionality to our cluster. And, we came across Apache Impala as an excellent tool for this purposes. 
    • Only CDP supported Apache Impala.
    • Impala had limitation to work with Parquet, Kudu, HBase. Before CDP 6.* there was no support for ORC file format with Impala.
  • Thus, we came to know about PrestoDB, which was built at Facebook, and was an excellent distributed SQL Engine for ad-hoc querying. 
    • It not only supported ORC but has connectors for multiple data sources.

A bit history of Presto - 
  • Developed at Facebook ( 2012)
  • Supported by Presto Foundation establish by Linux Foundation (2019)
    • Original Developers & Linux Foundation get into conflict on naming & branding.
  • Did a hard Fork of PrestoSQL, rebranded it as Trino ( Dec 2020)
    • Supported by non profit org - Trino Software Foundation. 

Initially, we got confused that PrestoSQL is renamed as Trino. But, later we found out that - 
  • Now, there are 2 separate variants-  PrestoDB & Trino.
  • And, certainly have different vision(s).
And, that lead to confusion - as which one to use. Below table depicts, a few high level changes that we could figure out - 

Presto

Trino

Apache License 2.0, supported by The Presto Foundation hosted by Linux Foundation

Apache License 2.0 and supported by the Trino Software Foundation.

Presto on YARN –

https://prestodb.io/presto-yarn/

Apache Slider was supported by HDP but not by CDP https://www.cloudera.com/products/open-source/apache-hadoop/apache-slider.html

Trino on YARN abandoned.

 

https://github.com/trinodb/trino/discussions/6794

https://ahana.io/presto-vs-trino/ - Trino not used at Facebook

False claims of Trino being used at Facebook

https://pandio.com/difference-between-trino-and-prestodb/#:~:text=While%20Trino%20is%20an%20excellent,makes%20Trino%20better%20than%20Presto.

PrestoDB still leading GitHub Stars

PrestoSQL/ Trino matching up with PrestoDB

https://ahana.io/presto-vs-trino/

Ahana still part of Presto Foundation and supporting PrestoDB

Starburst is  also member of Presto Foundation and managing conformance program with other members, to produce enterprise-grade distributions for Presto, which they develop from Trino. 

But, they still suggest that its same software - https://www.starburst.io/blog/prestosql-becomes-trino/

Less inclined towards creating new connectors. Refer - https://prestodb.io/docs/current/connector.html

Seemingly, Trino is more inclined towards creating new connectors, like they already have Atop, Ignite, Kinesis, SingleStore connector which are not there in PrestoDB

Refer - https://trino.io/docs/current/connector.html

 

Presto has worked towards performance

Gains, as listed below –

 

Aria - push down entire expressions to the data source for some file formats like ORC

 

Presto Unlimited - create temporary in-memory bucketed tables

 

dynamic SQL functions

 

Presto-on-Spark to get ETL Fault Tolerance.

 

RaptorX Project for Caching

 

Disaggregated Coordinator  for scaling horizontally.

 

There are more features which are developed by Ahana - https://ahana.io/presto-vs-trino/

Trino Lacks these developments of Presto. But, may be supported by Enterprise Starburst




Conclusion 
Trino seemed to be next popular buzz in the market at this time. It had increasing GitHub stars, more companies were inclined towards using Trino, Community Support is growing , etc. 

But, we choose PrestoDB over Trino, due to - 

  • Reliability and scalability.

  • We were no interested in new connectors, or docker / cloud at this moment. Our interest were with performance gains like RaptorX caching, Aria scan and predicate pushdown, and Presto on Spark ( for reliability and fault tolerance ) 
  • PrestoDB is hosted by Linux Foundation, giving confidence to us on usage. 

Cloudera added ORC support to Impala. It would be good to benchmark PrestoDB (ORC) against Impala (ORC) to see the right fit.  

Comments

Post a Comment

Popular posts

Hive Parse JSON with Array Columns and Explode it in to Multiple rows.

By Dinesh
 Say we have a JSON String like below -  { "billingCountry":"US" "orderItems":[       {          "itemId":1,          "product":"D1"       },   {          "itemId":2,          "product":"D2"       }    ] } And, our aim is to get output parsed like below -  itemId product 1 D1 2 D2   First, We can parse JSON as follows to get JSON String get_json_object(value, '$.orderItems.itemId') as itemId get_json_object(value, '$.orderItems.product') as product Second, Above will result String value like "[1,2]". We want to convert it to Array as follows - split(regexp_extract(get_json_object(value, '$.orderItems.itemId'),'^\\["(.*)\\"]$',1),'","') as itemId split(regexp_extract(get_json_object(value, '$.orderItems.product'),'^\\["(.*)\\"]$',1),&
Post a Comment
Read more




Read from a hive table and write back to it using spark sql

By Dinesh
In context to Spark 2.2 - if we read from an hive table and write to same, we get following exception- scala > dy . write . mode ( "overwrite" ). insertInto ( "incremental.test2" ) org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; 1. This error means that our process is reading from same table and writing to same table. 2. Normally, this should work as process writes to directory .hiveStaging... 3. This error occurs in case of saveAsTable method, as it overwrites entire table instead of individual partitions. 4. This error should not occur with insertInto method, as it overwrites partitions not the table. 5. A reason why this happening is because Hive table has following Spark TBLProperties in its definition. This problem will solve for insertInto met
Post a Comment
Read more




Caused by: java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary

By Dinesh
Exception -  Caused by: java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary at org.apache.parquet.column.Dictionary.decodeToBinary(Dictionary.java:44) at org.apache.spark.sql.execution.vectorized.ColumnVector.getUTF8String(ColumnVector.java:645) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) Analysis - This might occur because of data type mismatch between Hive Table & written Parquet file. Solution - Correct the data type to match between Hive Table & Parquet
Post a Comment
Read more




Hadoop Distcp Error Duplicate files in input path

By Dinesh
  One may face following error while copying data from one cluster to other, using Distcp  Command: hadoop distcp -i {src} {tgt} Error: org.apache.hadoop.toolsCopyListing$DulicateFileException: File would cause duplicates. Ideally there can't be same file names. So, what might be happening in your case is you trying to copy partitioned table from one cluster to other. And, 2 different named partitions have same file name. Your solution is to correct Source path  {src}  in your command, such that you provide path uptil partitioned sub directory, not the file. For ex - Refer below : /a/partcol=1/file1.txt /a/partcol=2/file1.txt If you use  {src}  as  "/a/*/*"  then you will get the error  "File would cause duplicates." But, if you use  {src}  as  "/a"  then you will not get error in copying.
Post a Comment
Read more




org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.;

By Dinesh
  Caused by: org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.; at org.apache.spark.sql.execution.command.DDLUtils$.verifyNotReadPath(ddl.scala:906) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:192) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:134) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis.apply(DataSourceStrategy.scala:134) at org.apache.spark.sql.execution.datasource
Post a Comment
Read more