I have tried connecting spark with JDBC connections to fetch data from MySQL / Teradata or similar RDBMS and was able analyse the data.
Can spark be used to store the data to HDFS?
Is there any possibility for spark outperforming
the activities of Sqoop.
Looking for you valuable answers and explanations.
There are two main things about Sqoop and Spark. The main difference is Sqoop will read the data from your RDMS doesn't matter what you have and you don't need to worry much about how you table is configured.
With Spark using JDBC connection is a little bit different how you need to load the data. If your database doesn't have any column like numeric ID or timestamp Spark will load ALL the data in one single partition. And then will try to process and save. If you have one column to use as partition than Spark sometimes can be even faster than Sqoop.
I would recommend you to take a look in this doc.enter link description here
The conclusion is, if you are going to do a simple export and that need to be done daily with no transformation I would recommend Sqoop to be simple to use and will not impact your database that much. Using Spark will work well IF your table is ready for that, besides that goes with Sqoop
Related
I use Spark 2.0.2.
While learning the concept of writing a dataset to a Hive table, I understood that we do it in two ways:
using sparkSession.sql("your sql query")
dataframe.write.mode(SaveMode."type of
mode").insertInto("tableName")
Could anyone tell me what is the preferred way of loading a Hive table using Spark ?
In general I prefer 2. First because for multiple rows you cannot build such a long sql and second because it reduces the chance of errors or other issues like SQL injection attacks.
In the same way that for JDBC I use PreparedStatements as much as possible.
Think in this fashion, we need to achieve updates on daily basis on hive.
This can be achieved in two ways
Process all the data of the hive
Process only effected partitions.
For the first option sql works like a gem, but keep in mind that the data should be less to process entire data.
Second option works well.If you want to process only effected partition. Use data.overwite.partitionby.path
You should write the logic in such a way that it process only effected partitions. This logic will be applied to tables where data is in millions T billions records
Currently, i am trying to adopt big data to replace my current data analysis platform. My current platform is pretty simple, my system get a lot of structured csv feed files from various upstream systems, then, we load them as java objects (i.e. in memory) for aggregation.
I am looking for using Spark to replace my java object layer for aggregation process.
I understandthat Spark support loading file from hdfs / filesystem. So, Hive as data warehouse seems not a must. However, i can still load my csv files to Hive first, then, use Spark to load data from Hive.
My question here is, in my situation, what's the pros / benefit if i introduce a Hive layer rather than directly loading the csv file to Spark DF.
Thanks.
You can always look and feel the data using the tables.
Adhoc queries/aggregation can be performed using HiveQL.
When accessing that data through Spark, you need not mention the schema of the data separately.
I have deployed a 9 node DataStax Cluster in Google Cloud. I am new to Cassandra and not sure how generally people push the data to Cassandra.
My requirement is read the data from flatfiles and RDBMs table and load into Cassandra which is deployed in Google Cloud.
These are the options I see.
1. Use Spark and Kafka
2. SStables
3. Copy Command
4. Java Batch
5. Data Flow ( Google product )
Is there any other options and which one is best.
Thanks,
For flat files you have 2 most effective options:
Use Spark - it will load data in parallel, but requires some coding.
Use DSBulk for batch loading of data from command line. It supports loading from CSV and JSON, and very effective. DataStax's Academy blog just started a series of the blog posts on DSBulk, and first post will provide you enough information to start with it. Also, if you have big files, consider to split them into smaller ones, as it will allow DSBulk to perform parallel load using all available threads.
For loading data from RDBMS, it depends on what you want to do - load data once, or need to update data as they change in the DB. For first option you can use Spark with JDBC source (but it has some limitations too), and then saving data into DSE. For 2nd, you may need to use something like Debezium, that supports streaming of change data from some databases into Kafka. And then from Kafka you can use DataStax Kafka Connector for submitting data into DSE.
CQLSH's COPY command isn't as effective/flexible as DSBulk, so I won't recommend to use it.
And never use CQL Batch for data loading, until you know how it works - it's very different from RDBMS world, and if it's used incorrectly it will really make loading less effective than executing separate statements asynchronously. (DSBulk uses batches under the hood, but it's different story).
I want to save and read the data from HBase from/to Spark.
I want to get the Dataframe abstraction as dataframe is best for memory management compared to RDD and it is convenient to do any processing.
I looked at possible candidates for getting Dataframe abstraction. One of them is Phoenix based solution. I do not want to have pheonix layer on top of HBase due to approvals. I searched for other solutions, but would want to know the best possibility that someone had tried.
We have a performant one at Splice Machine (Open Source). We wrote a separate InputFormat for HBase so we can read directly from store files in hbase vs. performing remote scans. The killer for Spark performance on top of hbase is the remote scan based InputFormat (i.e. how you read the data).
Sean Busbey at Cloudera has worked on a Spark HBase connector and here is a blog from HortonWorks on a similar idea...
http://hortonworks.com/blog/spark-hbase-dataframe-based-hbase-connector/
The "connectors" functionally work but perform poorly for large data sets.
Hope this helps and good luck.
I would like to hear your thoughts and experiences on the usage of CQL and in-memory query engine Spark/Shark. From what I know, CQL processor is running inside Cassandra JVM on each node. Shark/Spark query processor attached with a Cassandra cluster is running outside in a separated cluster. Also, Datastax has DSE version of Cassandra which allows to deploy Hadoop/Hive. The question is in which use case we would pick a specific solution instead of the other.
I will share a few thoughts based on my experience. But, if possible for you, please let us know about your use-case. It'll help us in answering your queries in a better manner.
1- If you are going to have more writes than reads, Cassandra is obviously a good choice. Having said that, if you are coming from SQL background and planning to use Cassandra then you'll definitely find CQL very helpful. But if you need to perform operations like JOIN and GROUP BY, even though CQL solves primitive GROUP BY use cases through write time and compact time sorts and implements one-to-many relationships, CQL is not the answer.
2- Spark SQL (Formerly Shark) is very fast for the two reasons, in-memory processing and planning data pipelines. In-memory processing makes it ~100x faster than Hive. Like Hive, Spark SQL handles larger than memory data types very well and up to 10x faster thanks to planned pipelines. Situation shifts to Spark SQL benefit when multiple data pipelines like filter and groupBy are present. Go for it when you need ad-hoc real time querying. Not suitable when you need long running jobs over gigantic amounts of data.
3- Hive is basically a warehouse that runs on top of your existing Hadoop cluster and provides you SQL like interface to handle your data. But Hive is not suitable for real-time needs. It is best suited for offline batch processing. Doesn't need any additional infra as it uses underlying HDFS for data storage. Go for it when you have to perform operations like JOIN, GROUP BY etc on large dataset and for OLAP.
Note : Spark SQL emulates Apache Hive behavior on top of Spark, so it supports virtually all Hive features but potentially faster. It supports the existing Hive Query language, Hive data formats (SerDes), user-defined functions (UDFs), and queries that call external scripts.
But I think you will be able to evaluate the pros and cons of all these tools properly only after getting your hands dirty. I could just suggest based on your questions.
Hope this answers some of your queries.
P.S. : The above answer is based on solely my experience. Comments/corrections are welcome.
There is a very good effort for benchmark documented here - https://amplab.cs.berkeley.edu/benchmark/