I'm using AWS DMS to migrate one table that has over 2B records into s3 parquet files.
My source is Oracle. The task is getting failed after running for around 6 hours with snapshot too old error. I see the data that it has copied in those 6 hours into the target s3 bucket (~900M).
Is there a way to fetch only the remaining records in DMS in my next run?
Related
Need help in processing incremental files.
Scenario: Source team is creating file in every 1hr in s3 (hrly partitioned). I would like to process in every 4hr. The Glue etl will read the s3 files (partitioned hrly) and process to store in different s3 folders.
Note : Glue ETL is called from airflow.
Question How can I make sure that I only process the incremental files ( let’s say 4 files in each execution)?
Sounds like a use case for Bookmarks
For example, your ETL job might read new partitions in an Amazon S3
file. AWS Glue tracks which partitions the job has processed
successfully to prevent duplicate processing and duplicate data in the
job's target data store.
I have 4 millions user data that is looked up by their phone number. Source data is in S3. Search response time is 50 millisec and system need to be available 99.995%.
I am thinking about a nightly job like below
S3 Source Data -> Glue ETL -> CSV file or RDS -> Cache Upload job -> AWS Redis global datastore
I am leaning towards RDS since in case upload job fails for some reason I don't want to restart from beginning. Every row uploaded to Redis will be mark as processed.
I was planning to do an initial load of data from S3 to Redis and then do incremental load from thereafter. The data team has informed me that they can't produce the incremental data, in other words they can't tell me what data changed yesterday.
As a result, every day I will have to empty the Redis cache and reload the entire data. Since this upload will take considerable time, I am wondering how to keep the response time to 50 ms while data is loading.
Will appreciate any ideas. Thanks!
I have around 15000 files (ORC) present in S3 where each file contain few minutes worth of data and size of each file varies between 300-700MB.
Since recursively looping through a directory present in YYYY/MM/DD/HH24/MIN format is expensive, I am creating a file which contain list of all S3 files for a given day (objects_list.txt) and passing this file as input to spark read API
val file_list = scala.io.Source.fromInputStream(getClass.getResourceAsStream("/objects_list.txt"))
val paths: mutable.Set[String] = mutable.Set[String]()
for (line <- file_list.getLines()) {
if(line.length > 0 && line.contains("part"))
paths.add(line.trim)
}
val eventsDF = spark.read.format("orc").option("spark.sql.orc.filterPushdown","true").load(paths.toSeq: _*)
eventsDF.createOrReplaceTempView("events")
The Size of the cluster is 10 r3.4xlarge machines (workers)(Where Each Node: 120GB RAM and 16 cores) and master is of m3.2xlarge config (
The problem which am facing is, spark read was running endlessly and I see only driver working and rest all Nodes aren't doing anything and am not sure why driver is opening each S3 file for reading, because AFAIK spark works lazily so till an action is called reading shouldn't happen, I think it's listing each file and collecting some metadata associated with it.
But why only Driver is working and rest all Nodes aren't doing anything and how can I make this operation to run in parallel on all worker nodes ?
I have come across these articles https://tech.kinja.com/how-not-to-pull-from-s3-using-apache-spark-1704509219 and https://gist.github.com/snowindy/d438cb5256f9331f5eec, but here the entire file contents are being read as an RDD, but my use case is depending on the columns being referred only those blocks/columns of data should be fetched from S3 (columnar access given ORC is my storage) . Files in S3 have around 130 columns but only 20 fields are being referred and processed using dataframe API's
Sample Log Messages:
17/10/08 18:31:15 INFO S3NativeFileSystem: Opening 's3://xxxx/flattenedDataOrc/data=eventsTable/y=2017/m=09/d=20/h=09/min=00/part-r-00199-e4ba7eee-fb98-4d4f-aecc-3f5685ff64a8.zlib.orc' for reading
17/10/08 18:31:15 INFO S3NativeFileSystem: Opening 's3://xxxx/flattenedDataOrc/data=eventsTable/y=2017/m=09/d=20/h=19/min=00/part-r-00023-5e53e661-82ec-4ff1-8f4c-8e9419b2aadc.zlib.orc' for reading
You can see below that only One Executor is running that to driver program on one of the task Nodes(Cluster Mode) and CPU is 0% on rest of the other Nodes(i.e Workers) and even after 3-4 hours of processing, the situation is same given huge number of files have to be processed
Any Pointers on how can I avoid this issue, i.e speed up the load and process ?
There is a solution that can help you based in AWS Glue.
You have a lot of files partitioned in your S3. But you have partitions based in timestamp. So using glue you can use your objects in S3 like "hive tables" in your EMR.
First you need to create a EMR with version 5.8+ and you will be able to see this:
You can set up this checking both options. This will allow to access the AWS Glue Data Catalog.
After this you need to add the your root folder to the AWS Glue Catalog. The fast way to do that is using the Glue Crawler. This tool will crawl your data and will create the catalog as you need.
I will suggest you to take a look here.
After the crawler runs, this will have the metadata of your table in the catalog that you can see at AWS Athena.
In Athena you can check if your data was properly identified by the crawler.
This solution will make your spark works close to a real HDFS. Due to the metadata will be properly in the Data Catalog. And the time you app is taking to find the "indexing" will allow to run the jobs faster.
Working with this here I was able to improve the queries, and working with partitions was much better with glue. So, have a try this probably can help in the performance.
I would like to use AWS EMR to query large log files that I will write to S3. I can design the files any way I like. The data is created in a rate of 10K entries/minute.
The logs consist of dozens of data points and I'd like to collect data for very long period of time (years) to compare trends etc.
What are the best practices for creating such files that will be stored on S3 and queried by AWS EMR cluster?
Whats the optimal file sizes ?Should I create separate files for example on hourly basis?
What is the best way to name the files?
Should I place them in daily/hourly buckets or all in the same bucket?
Whats the best way to handle things like adding some data after a while or change in data structure that I use?
Should I compress things for example by leaving out domain names out of urls or keep as much data as possible?
Is there any concept like partitioning (the data is based on 100s of websites so I can use site ids for example). I must be able to query all the data together, or by partitions.
Thanks!
in my opinion you should use a hourly basis bucket to store data in s3 and then use a pipeline to schedule your mr job to clean the data.
once u have clean the data you can keep it to a location in s3 and then you can run a data pipeline on hourly basis on the lag of 1hour with respect to your MR pipeline to put this process data into redshift.
Hence at 3am on a day you will have 3 hour of processed data in s3 and 2 hour processed into redshift dB.
To do this you can have 1 machine dedicated for running pipelines and on that machine you can define you shell script/perl/python or so script to load data to your dB.
You can use AWS bucketing formatter for year,month,date,hour and so on. for e.g.
{format(minusHours(#scheduledStartTime,2),'YYYY')}/mm=#{format(minusHours(#scheduledStartTime,2),'MM')}/dd=#{format(minusHours(#scheduledStartTime,2),'dd')}/hh=#{format(minusHours(#scheduledStartTime,2),'HH')}/*
For now i have copied Data from Amazon S3 to Amazon Redshift Using AWS Data Pipeline only for current date and time. I want to copy data from S3 to Redshift for every 30 minutes. And also the last processed S3 file name is stored into another Redshift table.
Could somebody answer this question ?
You can use the RedshiftCopyActivity data pipeline object to do exactly this. The schedule field in the RedshiftCopyActivity object accepts a data pipeline schedule object that can run on 30 minute intervals. You'll need to define a full pipeline in JSON including all your AWS resource info (Redshift data nodes, EC2 instances, S3 bucket & key). The filepath for the source data file in the JSON template could point to a static file which is overwritten every 30 minutes by whatever produces the data.