I am trying to design a sort of data pipeline to migrate my Hive tables into BigQuery. Hive is running on an Hadoop on premise cluster. This is my current design, actually, it is very easy, it is just a shell script:
for each table source_hive_table {
INSERT overwrite table target_avro_hive_table SELECT * FROM source_hive_table;
Move the resulting avro files into google cloud storage using distcp
Create first BQ table: bq load --source_format=AVRO your_dataset.something something.avro
Handle any casting issue from BigQuery itself, so selecting from the table just written and handling manually any casting
}
Do you think it makes sense? Is there any better way, perhaps using Spark?
I am not happy about the way I am handling the casting, I would like to avoid creating the BigQuery table twice.
Yes, your migration logic makes sense.
I personally prefer to do the CAST for specific types directly into the initial "Hive query" that generates your Avro (Hive) data. For instance, "decimal" type in Hive maps to the Avro 'type': "type":"bytes","logicalType":"decimal","precision":10,"scale":2
And BQ will just take the primary type (here "bytes") instead of the logicalType.
So that is why I find it easier to cast directly in Hive (here to "double").
Same problem happens to the date-hive type.
Related
I have started learning Hadoop.I understood that HDFS provides distributed storage system and Mapreduce is for data processing.Now i ma reading Hadoop ecosystem.
From the definition of Hive, it is a data ware house built on hadoop for providing SQL like interface.
My question is when hadoop provides HDFS which is falut tolerant , distributed then why hive? Does hive replaces HDFS?.
Does hive provide only sql interface or storage also?
Hive does not replace HDFS. Hive provides sql type interface to data that is stored in HDFS. Its basically used for querying and analysis of data that is stored. Hive in a sense actually eliminates a lot of boiler plate code, that you would have to write if you were using mapreduce. for example just think of how you are going to create different types of joins(left, right, bucketed) or group by clause or any other sql clause in mapreduce and you will get your answer (you lines of code will easily scale to 100's ). Hive provides them out-of-the-box. You dont need to write those lengthy programs in mapreduce. Hive already does that for you.
One thing to note is, Hive itself uses Mapreduce behind the scenes. So any group by, count, join is converted to mapreduce jobs only. You can change this though to Tez/Spark.
for your second question, hive does not provide any storage, it just uses a database (derby as default, MySQL would be a good choice if you want to use a different db) as a metastore just to store the metadata related to the tables, partitions, views, buckets etc.. (metadata is like location of tables, type of data stored in tables, partitions info of the tables, created date, modified date etc..) you create with hive.
To answer your question in comment...
Hive can process structured (csv,txt etc) data & semi-structured(xml,json,parquet etc). It cannot process unstructured data like audio, video etc.
Note: Semi structured data can be handled in DDLs and also through spark to be put into Hive.
I encourage you to learn what is external and managed tables in hive too.
Happy learning.
How to HBase table data to convert .CSV file, im trying to convert table data to csv format , but i couldn't get any code
hbase001> list
Table
sample_data
Creating an external Hive table mapped on to HBase table using HBaseStorageHandler can solve your problem ,you can now use "select * from table_name" to get data into a csv table (stored as textfile fields terminted by ','). Please refer the below link for reference.
https://cwiki.apache.org/confluence/display/Hive/HBaseIntegration#HBaseIntegration-Usage
There are plenty of ways to solve your task. You can use spark, regular mapreduce or special tools like sqoop. This task is rather trivial and you can implement it by yourself if you learn hadoop. The quickest way for starters to do it is probably sqoop. Please get youself familiar with this power tool and play with it.
Good luck!
What is the best ways to parallel ingest data from Teradata database into Hadoop with parallel data moving?
If we create a job which is simple opens one session to Teradata database it will take a lot of time to load huge table.
if we create a set of sessions to load data in parallel, and also make Select in each of the sessions, than it will make a set of Full table scans Teradata to produce a data
What is the recommended best practice to load data in parallelised streams and make unnecessary workload to Teradata?
If Tera data supports table partitioning like oracle, you could try reading the table based on partitioning points which will enable parallelism in read...
Other option you have is, split the table into multiple partitions like adding a where clause on indexed column. This will ensure index scan and you can avoid full table scan.
The most scalable way to ingest data into Hadoop form teradata, which i found is to use Teradata connector for hadoop. It is included in Cloudera & Hortonworks distributions. I will show example base on Cloudera documentation, but the same works with Hortonworks as well:
Informatica big Data edition is using standard Scoop invocation via command line and submitting set of parameters to it. So the main question is - which driver to use to make parallel connections between two MPP systems.
Here is the link to the Cloudera documentation:
Using the Cloudera Connector Powered by Teradata
And here is the digest from this documentation (You could find that this connector support different kinds of load balancing between connections):
Cloudera Connector Powered by Teradata supports the following methods for importing data from Teradata to Hadoop:
split.by.amp
split.by.value
split.by.partition
split.by.hash
split.by.amp Method
This optimal method retrieves data from Teradata. The connector creates one mapper per available Teradata AMP, and each mapper subsequently retrieves data from each AMP. As a result, no staging table is required. This method requires Teradata 14.10 or higher.
If you use partition names in the select clause, Power Center will select only the rows within that partition so there won't be duplicate read (don't forget to choose Database partitioning in Informatica session level). However if you use key range partition you have to choose the range as you mentioned in settings. Usually we use NTILE oracle analytical function to split the table into multiple portions so that the read will be unique across the selects. Please let me know if you have any question. If you have range/auto generated/surrogate key column in the table use it in where clause - write a sub-query to divide the table into multiple portions.
I was trying to use Hive to query the tables I saved using saveAsTable() provided by Spark DataFrame. Everything works well when I query using hiveContext.sql(). However, when I switch to hive and describe the table, it becomes col, array, something like this and is no longer queryable.
Any ideas how to work it through? Is there a reliable way to make Hive understands the metadata defined in spark instead of explicitly defining the schema?
Sometimes I make use of spark to infer schema from the raw data or read schema from certain file formats like parquet so don't want to create these table that could be inferred automatically.
Thanks a lot for any advice!
I've been doing some investigation lately around using Hadoop, Hive, and Pig to do some data transformation. As part of that I've noticed that the schema of data files doesn't seem to attached to files at all. The data files are just flat files (unless using something like a SequenceFile). Each application that wants to work with those files has its own way of representing the schema of those files.
For example, I load a file into the HDFS and want to transform it with Pig. In order to work effectively with it I need to specify the schema of the file when I load the data:
EMP = LOAD 'myfile' using PigStorage() as { first_name: string, last_name: string, deptno: int};
Now, I know that when storing a file using PigStorage, the schema can optionally be written out along side it, but in order to get a file into Pig in the first place it seems like you need to specify a schema.
If I want to work with the same file in Hive, I need to create a table and specify the schema with that too:
CREATE EXTERNAL TABLE EMP ( first_name string
, last_name string
, empno int)
LOCATION 'myfile';
It seems to me like this is extremely fragile. If the file format changes even slightly then the schema must be manually updated in each application. I'm sure I'm being naive but wouldn't it make sense to store the schema with the data file? That way the data is portable between applications and the barrier to using another tool would be lower since you wouldn't need to re-code the schema for each application.
So the question is: Is there a way to specify the schema of a data file in Hadoop/HDFS or do I need to specify the schema for the data file in each application?
It looks like you are looking for Apache Avro. With Avro your schema is embedded in your data, so you can read it without having to worry about schema issues and it makes schema evolution really easy.
The great thing about Avro is that it is completely integrated in Hadoop and you can use it with a lot of Hadoop sub-projects like Pig and Hive.
For example with Pig you could do:
EMP = LOAD 'myfile.avro' using AvroStorage();
I would advise looking at the documentation for AvroStorage for more details.
You can also work with Avro with Hive as described here but I have not used that personally but it should work the same way.
What you need is HCatalog which is
"Apache HCatalog is a table and storage management service for data
created using Apache Hadoop.
This includes:
Providing a shared schema and data type mechanism.
Providing a table abstraction so that users need not be concerned with where or how
their data is stored.
Providing interoperability across data processing tools such as Pig, Map Reduce, and Hive."
You can take a look at the "data flow example" in the docs to see exactly the scenario you are talking about
Apache Zebra seems to be the tool that could provide a common schema definition across mr, pig and hive. It has its own schema store. MR job can use its built in TableStore to write to HDFS.