In most cases, these inaccuracies are not acceptable. Is a five-minute window in a job that processes 60 minutes of data insignificant? The answer depends on the kind of application you are developing. For example, you could have late arrivals that impact the first five minutes and the last five minutes of a job that processes an hour worth of data offline in Hadoop. one hour), the inaccuracies caused by late and out of order arrival of data usually only impact the window of time at the edges of the interval. Let us dive into the reasons why developers lean towards using a duplicated (nearline+offline) processing model.Īt LinkedIn, many source event streams get sent to both the real time Samza-based stream processing system and to the Hadoop and Spark-based offline batch processing system.Ī common assumption is that since batch processing happens within a much larger window of time (e.g. ![]() Although LinkedIn uses Apache Samza for stream processing most of the discussion in this post is applicable to other streaming systems as well. It should be noted that this post is not meant to cover offline data analysis scenarios where existing Hadoop and Spark-based batch solutions work well. However, there are some key issues with the Lambda architecture: for example, the duplicative development effort in building the hot (nearline) and cold (offline) paths of their processing pipeline, additional overhead of reprocessing, the overhead of merging the results of the online and offline processing before serving. Lambda solves some important problems for stream processing applications. There has been a lot of prior material explaining Lambda, so I will skip going into the basics here. ![]() In this post, I will focus on the main reasons why people routinely use Lambda architecture in stream processing applications and suggest alternatives. This is the first in a series of posts where I will discuss some of the important problems that we have faced and are trying to solve at LinkedIn. There are many hard problems in stream processing. Real time event processing (stream processing) is not new, however it is now ubiquitous and has reached massive scale. It is an age where events are captured at scale and processed in real time. We live in an age where we want to know relevant things happening around the world as soon as they happen an age where digital content is updated instantly based on our likes and dislikes an age where credit card fraud, security breaches, device malfunctions and site outages need to be detected and remedied as soon as they happen.
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