We want to do Big Data Analytics on our data stored in Amazon Redshift (currently in Terabytes, but will grow with time).
Currently, it seems that all our Analytics can be done through Redshift queries (and hence, no distributed processing might be required at our end) but we are not sure if that will remain to be the case in future.
In order to build a generic system that should be able to cater our future needs as well, we are looking to use Apache Spark for data analytics.
I know that data can be read into Spark RDDs from HDFS, HBase and S3, but does it support data reading from Redshift directly?
If not, we can look to transfer our data to S3 and then read it in Spark RDDs.
My question is if we should carry out our Data Analytics through Redshift's queries directly or should we look to go with the approach above and do analytics through Apache Spark (Problem here is that Data Locality optimization might not be available)?
In case we do analytics through Redshift queries directly, can anyone please suggest a good Workflow Scheduler to write our Analytics jobs with. Our requirement is to be able to execute jobs as a DAG (Job2 should execute only if Job1 succeeds, etc) and be able to schedule our workflows through the proposed Workflow Engine.
Oozie seems like a good fit for our requirements but it turns out that Oozie cannot be used without Hadoop. Does it make sense to set up Hadoop on our machines and then use Oozie Workflow Scheduler to schedule our Data Analysis jobs through Redshift queries?
You cannot access data stored on Redshift nodes directly (each via Spark), only via SQL queries submitted the cluster as a whole.
My suggestion would be to use Redshift as long as possible and only take on the complexity of Spark/Hadoop when you absolutely need it.
If, in the future, you move to Hadoop then Cascading Lingual gives you the option of running your existing Redshift analytics more or less unchanged.
Regarding workflow, Oozie is not a good fit for Redshift. I would suggest you look at Azkaban (true DAG) or Luigi (uses a Python DSL).
Related
I want to use Spark SQL (installed on Machine 1) with connectors for different data stores like HBase, Hive, Cassandra, and MySQL (installed on Machine 2 to perform simple analytics like Min/Max, averaging, etc.
My Question: Is the processing of these queries done on Machine 1 or Spark SQL acts as just an interface to perform different analytics but on the data store end (ie. Machine 2)?
Yes and no. It depends on your spark job.
Spark SQL is a separate implementation. It is datastore agnostic. When you implement a spark sql job , spark transforms it into something called DAG.
It is a similar technique to a database query plan, but running completely on the spark cluster.
In case of simple min / max, it might be translated into a direct underlying store query. But it might also be translated into something which is preselecting bunch of records, then doing an own data processing. This way it is also possible to join and aggregate data from different data sources.
You can analyze the spark sql plan with common explain statement or via spark web ui.
I have millions of streaming parquet files being written . I want to support running ad hoc interactive queries for debugging and analytics purpose ( added bonus if i can run streaming queries for some real time monitoring of key metrics as well).
What is a scalable solution for supporting this.
The two ways I have observed is running spark sql interactively over millions of parquet files (not too familiar with spark ecosystem but does this mean running a spark job for every sql user submits or do i need to run some streaming job and submit queries somehow) and second being using a presto sql engine on top of parquet (not exactly sure how presto ingests new incoming parquet files).
Any recommendations or pros and cons of either approach . Any better solutions considering i have > ~10Tb data produced every day .
Let me address your use cases :
Support running ad hoc interactive queries for debugging and analytics purpose
I would recommend building a presto cluster if you care about minimizing the latency of your queries and are willing to invest in many machines with a large amount of memory.
Reason: Presto would run fully in-memory without touching disk (in most cases)
A Spark Cluster can also do the job, however, it won't be as fast as Presto. The advantage of Spark over presto is its fault tolerance capabilities and its ability to fail over to disk in case of out of memory conditions which may be important for you given that you have too much data.
Run streaming queries for some real-time monitoring of key metrics as well
As long as you have basic queries, you can build dashboards on top of Presto which could run these queries every x minutes.
Having a considerable amount of processing may be a good reason to look at Spark streaming if real-time monitoring is important.
If it isn't then you could build an ETL (using Spark) for calculating your metrics, storing the data as a new hive table and then expose for querying via Presto/SparkSQL again.
How presto ingests new incoming parquet files?
I'm now aware of your architecture, but in any case, you need to provide Presto with a Hive connection (Hive Metastore to be precise).
Hive provides Presto with few schemas attached to the directories where you ingest your data. Presto dynamically sees the new data by default. Spark is not different by the way.
Presto has nothing to do with data ingestion. It only starts its job once the data is there.
While exploring various tools like [Nifi, Gobblin etc.], I have observed that Databricks is now promoting for using Spark for data ingestion/on-boarding.
We have a spark[scala] based application running on YARN. So far we are working on a hadoop and spark cluster where we manually place required data files in HDFS first and then run our spark jobs later.
Now when we are planning to make our application available for the client we are expecting any type and number of files [mainly csv, jason, xml etc.] from any data source [ftp, sftp, any relational and nosql database] of huge size [ranging from GB to PB].
Keeping this in mind we are looking for options which could be used for data on-boarding and data sanity before pushing data into HDFS.
Options which we are looking for based on priority:
1) Spark for data ingestion and sanity: As our application is written and is running on spark cluster, we are planning to use the same for data ingestion and sanity task as well.
We are bit worried about Spark's support for many datasources/file types/etc. Also, we are not sure if we try to copy data from let's say any FTP/SFTP then will all workers will write data on HDFS in parallel? Is there any limitation while using it? Is there any Audit trail maintained by Spark while this data copy?
2) Nifi in clustered mode: How good Nifi would be for this purpose? Can it be used for any datasource and for any size of file? Will be maintain the Audit trail? Would Nifi we able to handle such large files? How large cluster would be required in case we try to copy GB - PB of data and perform certain sanity on top of that data before pushing it to HDFS?
3) Gobblin in clustered mode: Would like to hear similar answers as that for Nifi?
4) If at all there is any other good option available for this purpose with lesser infra/cost involved and better performance?
Any guidance/pointers/comparisions for above mentioned tools and technologies would be appreciated.
Best Regards,
Bhupesh
After doing certain R&D and considering the fact that using NIFI or goblin will demand for more infrastructure cost. I have started testing Spark for data on-boarding.
SO far I have tried using Spark job for importing data [present at a remote staging area/node] into my HDFS and I am able to do that by mounting that remote location with all my spark cluster worker nodes. Doing this made that location local to those workers, hence spark job ran properly and data is on-boarded to my HDFS.
Since my whole project is going to be on Spark, hence keeping data on-boarding part on spark would not cost anything extra to me. So far I am going good. Hence I would suggest to others as well, if you already have spark cluster and hadoop cluster up and running then instead of adding extra cost [where cost could be a major constraint] go for spark job for data on-boarding.
I've been reading about Apache Cassandra lately to learn how it works and how to use it for IoT projects, especially in the need of time series based database..
However, I started to notice that Apache Spark is often mentioned when people talk about Cassandra too.
The question is, as long as I can use Cassandra cluster of nodes to serve my app, to store and read data, why would I need Apache Spark? any useful use-cases are appreciated!
The answer is broad but summarizing ... Cassandra is highly scalable and there are lot of scenarios where it fits but CQL sintax has some limitations if you don't have your schema ready for some queries.
If you want to make use of your data without restrictions and doing analytical workloads with your cassandra data or join with other tables Spark is the most appropriate complement. Spark has a tight integration with Cassandra.
I recommend you to check this slides: http://www.slideshare.net/patrickmcfadin/apache-cassandra-and-spark-you-got-the-the-lighter-lets-start-the-fire?qid=48e2528c-a03c-49b4-879e-45599b2aff34&v=&b=&from_search=5
Cassandra is for storing data where as Spark is for performing some computation on top of it. Analogy with Hadoop: Cassandra is like HDFS where as Spark is like Map Reduce.
Especially with computations, when using DataStax Cassandra connector, data locality can be exploited. If you need to do some computation which modifies a row (but doesn't really depend on anything else), then that operation is optimized to run locally on each machine in cluster without any data movement in network.
Same goes with a lot of other Spark workload, the actions(some function which modifies the data) are done locally and only result is sent to client. As far as I know, when you want to do analytics on top of data stored in Cassandra, Spark is well supported and popular choice. If you don't need to do any operations on the data, still you can use Spark for other purposes like I mentioned below.
Spark streaming can be used to ingest or export data from Cassandra ( I used it a lot personally). The same data import/export can be achieved with small hand-written JDBC agents but Spark streaming code I wrote for ingesting 10GB data from Cassandra contains less than 20 lines of code with multi machine-multi threading built-in and an admin UI where I can see the job progress.
With Spark+Zeppelin, we can visualize Cassandra data using Spark, we can build beautiful UIs with little Spark code where users can even enter input and see the result as graph/table etc.
Note: Actually, visualization can be better with Kibana/ElasticSearch or Solr/Banana when used with Cassandra but they are very hard to setup and indexing has it's own issues to deal with.
There are a lot of other use cases, but personally I used Spark as a Swiss army knife for multiple tasks.
Apache cassandra is have feature like fast read and write so you can use it with the apache spark streaming to write your data directly into cassandra without legacy.
For use case you can consider any video application to upload video with the help of streaming and directly store it into cassandra blob.
I am surveying Google Dataflow and Apache Spark to decide which one is more suitable solution for our bigdata analysis business needs.
I found there are Spark SQL and MLlib in the spark platform to do structured data query and machine learning.
I wonder is there any corresponding solution in the Google Dataflow platform?
It would help if you could expand a bit on your specific use case(s). What are you trying to accomplish in relation to "Bigdata analysis"? The short answer... it depends :-)
Here are some key architectural points to consider in relation to Google Cloud Dataflow v. Spark and Hadoop MR.
Resource Mgmt: Cloud Dataflow is a completely on demand execution environment. Specifically - when you execute a job in Dataflow the resources are allocated on demand for that job only. There is no sharing/contention of resources across jobs. In comparison to a Spark or MapReduce cluster you would typically deploy a cluster of X nodes and then submit jobs and then tune the node resources across jobs. Of course you can build up and tear down these clusters, but the Dataflow model is geared towards hands free dev ops in relation to resource management. If want to optimize resource usage to job demands Dataflow is a solid model to control cost and nearly forget about resource tuning. If you prefer a multi-tenant style cluster I'd suggest you look at Google Cloud Dataproc as it provides the on demand cluster management aspects like Dataflow, but focused on class Hadoop workloads like MR, Spark, Pig, ...
Interactivity: Currently Cloud Dataflow does not provide an interactive mode. Meaning once you submit a job the work resources are bound to the graph that was submitted AND the majority of the data is loaded into resources as needed. Spark can be a better model if you want to load data into the cluster via in memory RDD's and then dynamically execute queries. The challenge is that as your data sizes and query complexity increases you will have to handle the devOps. Now if most of your queries can be expressed in SQL syntax you may want to look at BigQuery. BigQuery provides the "on demand" aspects of Dataflow and enables you to interactively execute queries over massive amounts of data e.g petabytes. The biggest advantage in my opinion of BigQuery is that you do not have think/worry about hardware allocation to deal with your data sizes. Meaning as your data sizes grow you don't have to think about hardware (memory and disk size) configuration.
Programming Model: Dataflow's programming model is functionally biased vs. a classic MapReduce model. There are many similarities between Spark and Dataflow in terms of API primitives. Things to consider: 1) Dataflow's primary programming language is Java. There is a Python SDK in the works. The Dataflow Java SDK in open sourced and has been ported to Scala. Today, Spark has more SDK surface choice with GraphX, Streaming, Spark SQL, and ML. 2) Dataflow is a unified programming model for batch and streaming based DAG development. The goal was to remove the complexity and cost switching when moving between batch and streaming models. The same graph can seamlessly run in either mode. 3) Today, Cloud Dataflow does not support converging/iterative based graph execution. If you need the power of something like MLib then Spark is the way to go. Keep in mind this is the state of things today.
Streaming & Windowing: Dataflow (building on top of the unified programming model) was architected to be a highly reliable, durable, and scalable execution environment for streaming. One of the key differences between Dataflow and Spark is that Dataflow enables you to easily process data in terms of its true event time vs. solely processing it at it's arrival time into the graph. You can window data into fixed, sliding, session or custom windows based on event time or arrival time. Dataflow also provides Triggers (applied to Windows) that enable you to control how you want to handle late arriving data. Net-net you dial in the level of correctness control to meet the needs of your analysis. For example, lets say you have a mobile game that interacts with a 100 edge nodes. These nodes create 10000's events second related to game play. Let's say a group of nodes can't communicate with your back end streaming analysis system. In the case of Dataflow - once that data does arrive - you can control how you'd like to handle the data in relation to your query correctness needs. Dataflow also provides the ability to upgrade your streaming jobs while they are in flight. For example, let's say you discover a logical bug in a transform. You can upgrade your in flight job without losing your existing Windowed state. Net-net you can keep you business running.
Net-net:
- if you are really primarily doing ETL style work (filtering, shaping, joining, ...) or batch style MapReduce Dataflow is a great path if you want minimal devOps.
- if you need to implement ML style graphs, go the Spark path and give Dataproc a try
- if you are doing ML and you first need to do ETL to clean up your training data implement a hybrid with Dataflow and Dataproc
- if you need interactivity Spark is a solid choice, but so is BigQuery if you are/can express your queries in SQL
- if you need to process your ETL and or MR jobs over streams, Dataflow is a solid choice.
So... what are you scenarios?
I've tried both :
Dataflow is still very young, the is no "out-of-the-box" solution for doing ML with it (even though you could implement algorithms in transforms), you could output the processes data to cloud storage and read it later with another tool.
Spark would be recommended but you would have to manage your cluster yourself.
However there is a good alternative: Google Dataproc
You can develop analysis tools with spark and deploy them with one command on your cluster, dataproc will manage the cluster itself without having to tweak the configuration.
I have built code using spark,DataFlow .Let me put my thoughts.
Spark/DataProc: I have used spark (Pyspark) a lot for ETL. You can use SQL and any programming language of your choice. Lot of functions are available (Including Window functions). Build your dataframe and write your transformation and it can be super fast. Once data is cached , any operation on the Dataframe will quick.
You can simply build hive external table on the GCS. Then you can use Spark for ETL and Load data into Big Query. This is for Batch processing.
For streaming you can use spark Streaming and load data into Big query.
Now if you have cluster allready then you have think whether to move to Google cloud or not. I found Data proc (Google Cloud Hadoop/Spark) offering is better as you don't have to worry many cluster managements..
DataFlow : It's know as apache beam. Here you can write your code in Java/Python or any other language. You can execute the code in any framework (Spark/MR/Flink).This is a unified model. Here you can do both batch processing and Stream Data processing.
google now offers both programming models- mapreduce and spark.
Cloud DataFlow and Cloud DataProc they are respectively