I have created a real time application in which I am writing data streams to hdfs from weblogs using flume, and then processing that data using spark stream. But while flume is writing and creating new files in hdfs spark stream is unable to process those files. If I am putting the files to hdfs directory using put command spark stream is able to read and process the files. Any help regarding the same will be great.
You have detected the problem yourself: while the stream of data continues, the HDFS file is "locked" and can not be read by any other process. On the contrary, as you have experienced, if you put a batch of data (that's yur file, a batch, not a stream), once it is uploaded it is ready for being read.
Anyway, and not being an expert on Spark streaming, it seems from the Spark Streaming Programming Guide, Overview section, that you are not performing the right deployment. I mean, from the picture shown there, it seems the streaming (in this case generated by Flume) must be directly sent to Spark Streaming engine; then the results will be put in HDFS.
Nevertheless, if you want to maintain your deployment, i.e. Flume -> HDFS -> Spark, then my suggestion is to create mini-batches of data in temporal HDFS folders, and once the mini-batches are ready, store new data in a second minibatch, passing the first batch to Spark for analysis.
HTH
In addition to frb's answer: which is correct - SparkStreaming with Flume acts as an Avro RPC Server - you'll need to configure an AvroSink which points to your SparkStreaming instance.
with spark2, now you can connect directly your spark streaming to flume, see official docs, and then write once on HDFS at the end of the process.
import org.apache.spark.streaming.flume._
val flumeStream = FlumeUtils.createStream(streamingContext, [chosen machine's hostname], [chosen port])
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I have an issue with small files and HDFS.
Scenario: I am using NiFi to read messages from the Kafka topic, these are all really small.
Requirement: to store these raw messages of data in HDFS(for replay capability)...before doing further processing on them.
I was thinking using Hadoop Archive (HAR) on them periodically. Is that something i can do through NiFi? the har command seems like a command line thing rather than something that i could execute through Nifi? Would love to know a solution that can achieve my requirement, without bringing down HDFS due to the small files.
Ginil
You can execute command line inside Nifi with ExecuteProcess processor :
http://nifi.apache.org/docs/nifi-docs/components/org.apache.nifi/nifi-standard-nar/1.6.0/org.apache.nifi.processors.standard.ExecuteProcess/
You can also take a look at Kafka-connect HDFS for putting kafka records into HDFS.
Some context to my question.
As you can see here:
https://medium.com/airbnb-engineering/data-infrastructure-at-airbnb-8adfb34f169c
There are 2 "doors" to load data into HDFS
Sqoop
Kafka
Using this topology as an example, what will be the best practice to load batch offline data which is hosted on an FTP server info HDFS?
Let's also assume that no changes are needed to perform on the file, we need to store it in HDFS in the same structure it is stored in the FTP server.
Thoughts?
Kafka isn't exactly configured to transfer "file sized" data by default. At least, not entire files in one message. Maybe break the lines apart, but then you need to reorder them and put them back together in HDFS.
In my experience, I've seen a few options from an FTP server.
Vanilla Hadoop, no extra software required
Use an NFS Gateway, WebHDFS or HttpFS to copy files directly to HDFS as if it were another filesystem
Additional Software required
Your own code with an FTP and HDFS client connection
Spark Streaming w/ an FTP Connector and HDFS write output
Kafka & Kafka Connect with an FTP Connector source and HDFS Sink
A Flume agent running on the FTP Server with an HDFS sink
Apache NiFi with a GetFTP and PutHDFS processor
Streamsets Data Collector doing something similar to NiFi (don't know the terms for this one)
we need to store it in HDFS in the same structure it is stored in the FTP server.
If these are small files, you're better off at least compressing the files into a Hadoop supported archive format before uploading to HDFS
I have a spark code that runs on a yarn cluster and converts csv to parquet using databricks library.
It works fine when the csv source is hdfs. But when the csv source is non-hdfs, which is usually the case, I come across this exception.
It should not happen as the same code works for hdfs csv source.
Complete link to the issue :
https://issues.apache.org/jira/browse/SPARK-19344
As discussed in the comments.
When the files are on the driver node, but not access-able by the nodes, the read will fail.
When using reading input file (e.g. spark.read in spark 2.0), the files should be be access by all executors nodes (e.g. when the files are on HDFS, cassandra, etc)
I don't know how to build architecture for following use case:
I have an Web application where users can upload files(pdf&pptx) and directories to be processed. After upload is complete web application put this files and directories in HDFS, then send a messages on kafka with path to this files.
Spark Application read messages from kafka streaming, collect them on master(driver), and after that process them. I collect messages first because i need to move the code to data, and not move data where the message is received. I understood that spark assign job to executor which already have file locally.
I have issues with kafka because i was forced to collect them first for the above reason, and when want to create checkpoint app crash "because you are attempting to reference SparkContext from a broadcast variable" even if the code run before adding checkpointing( I use sparkContext there because i need to save data to ElasticSearch and PostgreSQL. I don't know how exactly i can do code upgrading in this conditions.
I read about hadoop small files problems, and I understand what problems are in this case. I read that HBase is a better solution to save small files than just save in hdfs. Other problem in hadoop small files problems is big number of mappers and reducers created for computation, but i don't understand if this problem there in spark.
What is the best architecture for this use case?
How to do Job Scheduling? It's kafka good for that? or I need to use other service like rabbitMQ or something else?
Exist some method to add jobs to an running Spark application through some REST API?
How is the best way to save files? Is better to use Hbase because i have small files(<100MB)? Or I need to use SequenceFile? I think SequenceFile isn't for my use case because i need to reprocess some files randomly.
What is the best architecture do you think for this use case?
Thanks!
There is no one single "the best" way to build architecture. You need to make decisions and stick to them. Make the architecture flexible and decoupled so that you can easily replace components if needed.
Consider following stages/layers in your architecture:
Retrieval/Acquisition/Transport of source data (files)
Data processing/transformation
Data archival
As a retrieval component, I would use Flume. It is flexible, supports a lot of sources, channels (including Kafka) and sinks. In your case you can configure source that monitors the directory and extracts the newly received files.
For data processing/transformation - it depends what task you are solving. You probably decided on Spark Streaming. Spark streaming can be integrated with Flume sink (http://spark.apache.org/docs/latest/streaming-flume-integration.html) There are other options available, e.g. Apache Storm. Flume combines very well with Storm. Some transformations can also be applied in Flume.
For data archival - do not store/archive the files directly in Hadoop, unless they are bigger than few hundredths of megabytes. One solution would be to put them in HBase.
Make your architecture more flexible. I would place processed files in a temporary HDFS location and have some job regualarly archive them into zip, HBase, Hadoop Archive (there is such an animal) or any other solution.
Consider using Apache NiFi (aka HDF - Hortonworks Data Flow). It uses internally queues, provides a lot of processors. It can make your life easier and get the workflow developed in minutes. Give it a try. There is nice Hortonworks tutorial which , combined with HDP Sandbox running on a virtual machine/Docker, can bring you up to speed in very short time (1-2 hours?).
I need a system to analyze large log files. A friend directed me to hadoop the other day and it seems perfect for my needs. My question revolves around getting data into hadoop-
Is it possible to have the nodes on my cluster stream data as they get it into HDFS? Or would each node need to write to a local temp file and submit the temp file after it reaches a certain size? and is it possible to append to a file in HDFS while also running queries/jobs on that same file at the same time?
Fluentd log collector just released its WebHDFS plugin, which allows the users to instantly stream data into HDFS. It's really easy to install with ease of management.
Fluentd + Hadoop: Instant Big Data Collection
Of course you can import data directly from your applications. Here's a Java example to post logs against Fluentd.
Fluentd: Data Import from Java Applications
A hadoop job can run over multiple input files, so there's really no need to keep all your data as one file. You won't be able to process a file until its file handle is properly closed, however.
HDFS does not support appends (yet?)
What I do is run the map-reduce job periodically and output results to an 'processed_logs_#{timestamp}" folder.
Another job can later take these processed logs and push them to a database etc. so it can be queried on-line
I'd recommend using Flume to collect the log files from your servers into HDFS.