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BQ load Avro files with different schemas, field additions only

Context:
We have a Dataflow job that writes Avro files to GCS with a schema that changes weekly (field additions only). So that means, under a GCS prefix, we have a bunch of Avro files with different schemas, most likely 2 schemas at any given time. For more details, please see the Context section in this post.
The problem:
According to this SO post, when loading Avro files with multiple schemas into BigQuery, BigQuery will choose the file with the largest lexico order. However, this is not the behavior I observed. I am observing inconsistent behaviors.
In my first try, my newer schema was picked up and the new fields are there. However, the BQ load itself took much longer than it should. It took 7 minutes to load 368,594 records.
In my second try, the files with larger lexico order are using the new schema, and I am able to open the Avro file and see the new fields in the header. But when I BQ load these files into a table, the added fields are missing. But if I load the file with the largest order individually, the table will have the new fields.
We have a custom file naming policy, which is:
"chunk-$windowStart-$windowEnd-shardIndex-of-shardNum-UUID.avro"
Question:
Since BQ does auto schema detection for all Avro files, what exactly is the rule regarding old/new schemas? Especially when only field additions happen?
Why BQ load took so long in my first attempt? Did it load with the old schema, then found out about the new schema halfway, then re-did all the work?
Any suggestions on how I can debug this?

Google Cloud Support here!
Schema autodetection is an inference process which BigQuery carries out based on a small sample of rows. This means that the inferred schema may vary depending on the sample analysed. This may account for why you have experienced an inconsistent behaviour. For more info, check out this doc
To answer to this question I need more information, so I would encourage you to open a ticket with Google Cloud Support so that we can better assist you.
See answer 2.
I hope that helps.

Change file metadata using Apache Beam on a cloud database?

Can you change the file metadata on a cloud database using Apache Beam? From what I understand, Beam is used to set up dataflow pipelines for Google Dataflow. But is it possible to use Beam to change the metadata if you have the necessary changes in a CSV file without setting up and running an entire new pipeline? If it is possible, how do you do it?

You could code Cloud Dataflow to handle this but I would not. A simple GCE instance would be easier to develop and run the job. An even better choice might be UDF (see below).
There are some guidelines for when Cloud Dataflow is appropriate:
Your data is not tabular and you can not use SQL to do the analysis.
Large portions of the job are parallel -- in other words, you can process different subsets of the data on different machines.
Your logic involves custom functions, iterations, etc...
The distribution of the work varies across your data subsets.
Since your task involves modifying a database, I am assuming a SQL database, it would be much easier and faster to write a UDF to process and modify the database.

First, Apache Beam does not currently support schema update yet. There is a feature request for some times but no news
Another option is to alter your current dataflow written with Apache Beam pipeline to migrate your table to another (corrected schema) table. This, unfortunately, is not scale if you have a lot of data and plus if you need to frequently change table schema ( renaming columns, renaming table name, changing data types, ..etc).
What I propose is issue SQL queries to update your table schema instead. You can write a bash script using this guide that executes ALTER TABLE statement.

Migrating data from postgresql to hdfs using a java code

I am new to Hadoop and have been given the task of migrating structured data to HDFS using a java code. I know the same can be accomplished by Sqoop, but that is not my task.
Can someone please explain a possible way to do this.
I did attempt to do it. What I did was copy data from psql server using jdbc driver and then store it in a csv format in HDFS. Is this the right way to go about this?
I have read that Hadoop has its own datatypes for storing structured data. Can you please explain as to how that happens.
Thank you.

The state of the art is using (pull ETL) sqoop as regular batch processes to fetch the data from RDBMs. However, this way of doing is both resource consuming for the RDBMS (often sqoop run multiple thread with multiple jdbc connections), and takes long time (often you run sequential fetch on the RDBMS), and lead to data corruptions (the living RDBMS is updated while this long sqoop process is "always in late").
Then some alternative paradigm exists (push ETL) and are maturing. The idea behind is to build change data capture streams that listen the RDBMS. An example project is debezium. You can then build a realtime ETL that synchronize the RDBMS and the datawarehouse on hadoop or elsewhere.

Sqoop is a simple tool which perform following.
1) Connect to the RDBMS ( postgresql) and get the metadata of the table and create a pojo(a Java Class) of the table.
2) Use the java class to import and export through a mapreduce program.
If you need to write plain java code (Where parallelism you need to control for performance)
Do following:
1) Create a Java Class which connects to RDBMS using Java JDBC
2) Create a Java Class which accepts input String( Get from resultset) and write to HDFS service into a file.
Otherway doing this.
Create a MapReduce using DBInputFormat pass the number of input splits with TextOutputFormat as output directory to HDFS.
Please visit https://bigdatatamer.blogspot.com/ for any hadoop and spark related question.
Thanks
Sainagaraju Vaduka

You are better off using Sqoop. Because you may end up doing exactly what Sqoop is doing if you go the path of building it yourself.
Either way, conceptually, you will need a custom mapper with custom input format with ability to read partitioned data from the source. In this case, table column on which the data has to be partitioned would be required to exploit parallelism. A partitioned source table would be ideal.
DBInputFormat doesn't optimise the calls on source database. Complete dataset is sliced into configured number of splits by the InputFormat.
Each of the mappers would be executing the same query and loading only the portion of the data corresponding to split. This would result in each mapper issuing the same query along with sorting of dataset so it can pick its portion of data.
This class doesn't seem to take advantage of a partitioned source table. You can extend it to handle partitioned tables more efficiently.
Hadoop has structured file formats like AVRO, ORC and Parquet to begin with.
If your data doesn't require to be stored in a columnar format (used primarily for OLAP use cases where only few columns of large set of columns is required to be selected ), go with AVRO.

The way you are trying to do is not a good one because you are going to waste so much of time in developing the code,testing etc., Instead use sqoop to import the data from any RDBMS to hive. The first tool which has to come in our mind is Sqoop(SQL to Hadoop).

Is there a way to insert data to Oracle using Direct Path loading for in-memory records?

New to Oracle here but I have now read about the various bulk insert options for Oracle. In essence, true bulk loading is done using Direct Path loading mechanism via SQL*Loader. There's also APPEND hint options that use serial or parallel Direct Path loading. But each of these have the following limitations -
SQL*Loader works off of a Control File, which contains the path of the data file. In my case, there is no file.
APPEND hint option for INSERT can only use the syntax - insert into select from. In my case, the source data is not in any table.
Source of my data is actually a Spark dataframe. I am looking for options to push this data in chunks to Oracle tables, but using Direct Path loading option. For example, in Postgres, the PGConnection interface provides getCopyAPI.copyIn functionality and you can create a huge serialized blob than can be sent over as one big chunk using COPY tableName FROM STDIN yourBlob command. I am unable to find anything similar Java API for Oracle that works on in-memory records and is able to push data directly (without any insert statements).
Any ideas on how to achieve this? Anyone done this before?
In general, how do folks using Oracle and Spark push data to Oracle from a dataframe in an optimized way?
Thanks in advance!

Hadoop's Hive/Pig, HDFS and MapReduce relationship

My understanding of Apache Hive is that its a SQL-like tooling layer for querying Hadoop clusters. My understanding of Apache Pig is that its a procedural language for querying Hadoop clusters. So, if my understanding is correct, Hive and Pig seem like two different ways of solving the same problem.
My problem, however, is that I don't understand the problem they are both solving in the first place!
Say we have a DB (relational, NoSQL, doesn't matter) that feeds data into HDFS so that a particular MapReduce job can be run against that input data:
I'm confused as to which system Hive/Pig are querying! Are they querying the database? Are they querying the raw input data stored in the DataNodes on HDFS? Are they running little ad hoc, on-the-fly MR jobs and reporting their results/outputs?
What is the relationship between these query tools, the MR job input data stored on HDFS, and the MR job itself?

Apache Pig and Apache Hive load data from the HDFS unless you run it locally, in which case it will load it locally. How does it get the data from a DB? It does not. You need other framework to export the data in your traditional DB into your HDFS, such as Sqoop.
Once you have the data in your HDFS, you can start working with Pig and Hive. They never query a DB. In Apache Pig, for example, you could load your data using a Pig loader:
A = LOAD 'path/in/your/HDFS' USING PigStorage('\t');
As for Hive, you need to create a table and then load the data into the table:
LOAD DATA INPATH 'path/in/your/HDFS/your.csv' INTO TABLE t1;
Again, the data must be in the HDFS.
As to how it works, it depends. Traditionally it has always worked with a MapReduce execution engine. Both Hive and Pig parse the statements you write in PigLatin or HiveQL and translate it into an execution plan consisting of a certain number of MapReduce jobs, depending on the plan. However, now it can also translate it into Tez, a new execution engine which perhaps is too new to work correctly.
Why the need of Pig or Hive? Well, you really don't need these frameworks. Everything they can do, you can do it as well writing your own MapReduce or Tez jobs. However, writing for instance a JOIN operation in MapReduce might take hundreds or thousands of lines of code (really), while it is only one single line of code in Pig or Hive.

I dont think you can query any data with Hive/Pig without actually adding to them. So first you need to add data. And this data can be coming from any place and you just give the path for the data to be picked or add directly to them. Once data is in place, the query fetches the data only from those tables.
Underneath, they use map reduce as a tool to do the process. If you just have on the go data lying somewhere and need analysis, you can directly go to map redue and define your own logic. Hive is mostly at the SQL front. So you get querying features similar to SQL, and at the backend, map reduce does the job. Hope this info helps

Im not agree with that Pig and Hive solve the same problem, Hive is for querying data stored on hdfs as external or internal tables, Pig is for managing data flow stored on hdfs in a Directed Acyclic Graph, this is their main goals and we dont care about other uses, here i want to make difference between :
Querying data (the main purpose of Hive) which is getting answers to some questions about your data, for example : How many distinct user visiting my website per mounth in this year.
Managing a data flow (the main purpose of Pig) is to make your data go from initial state to have at the end a different state through transformations, for example : Data in location A filtered by critiria c joined with data in location B stored in location C.

Smeeb, Pig,Hive does same thing , I mean processing data which comes in files or what ever format.
here if you want to process data present in RDMS, first get that data to HDFS with help of Sqoop(SQL+HADOOP).
Hive used HQL like SQL to process, Pig uses kind flow with help of piglatin.
Hive stores all input data in tables format so, first thing before load data to Hive create a hive table, that structure (metadata) will be stored in any RDMS(Mysql). Then load with LOAD DATA INPATH 'path/in/your/HDFS/your.csv' INTO TABLE t1;