In the previous post, we have monitored our Kafka matrices using Prometheus and visualize the health of Kafka over Grafana. Now we will set an alert, so whenever any of Kafka broker is down, we’ll receive a notification.
Python is widely used programming language when it comes to Data science workloads and Python has way too many different libraries to back this fact. Most of the data scientists are familiar with Python and pandas mostly. But the main issue with Pandas is it works great for small and medium datasets but not so good on big-data workloads. The challenge now becomes to convert the existing
pysparkcode. This is just not straight forward and has a lot of performance hits if python UDFs are used without much care.
Koalas tries to address the first problem ie lessen the friction of learning different APIs to port their existing Pandas code to Pyspark. With Koalas, we can just directly replace the existing pandas code with Koalas. As far as the performance goes, there are no numbers yet as it is still in the initial phase of the project. But this definitely looks promising though.
Read on for some initial thoughts on the product, including a few gotchas.
This doesn’t draw the line exactly where the method changed from private to public, but generally speaking:
– gson-2.2.4.jar: the method is private, and therefore too old for use here
– gson-2.6.1: the method is public, and works fine.
– Somewhere between the two, the method’s status changed.
So, because I had some functionality that required the method be public and accessible, it was important I specify the right version in my dependency manager (SBT). “That’s easy,” I thought. “No problem.”
Spoilers: there was a problem.
Apache Kafka has become an essential component of enterprise data pipelines and is used for tracking clickstream event data, collecting logs, gathering metrics, and being the enterprise data bus in a microservices based architectures. Kafka is essentially a highly available and highly scalable distributed log of all the messages flowing in an enterprise data pipeline. Kafka supports internal replication to support data availability within a cluster. However, enterprises require that the data availability and durability guarantees span entire cluster and site failures.
The solution, thus far, in the Apache Kafka community was to use MirrorMaker, an external utility, that helped replicate the data between two Kafka clusters within or across data centers. MirrorMaker is essentially a Kafka high-level consumer and producer pair, efficiently moving data from the source cluster to the destination cluster and not offering much else. The initial use case that MirrorMaker was designed for was to move data from clusters to an aggregate cluster within a data center or to another data center to feed batch or streaming analytics pipelines. Enterprises have a much broader set of use cases and requirements on replication guarantees.
Read on for the list of benefits and upcoming features.
The first step is to dynamically get the list of clusters and their IPs. Hadoop clusters are often reprovisioned, added and terminated, so you cannot use the static list and addresses. In case of Amazon EMR, you can use the following Linux shell command to get the list of active clusters:
aws emr list-clusters --active
From its output you can get the cluster IDs and names. As a cluster ID and IP can change over time, its name is usually permanent (like
Adhoc-Analyticscluster) so it can be useful for various aggregation reports.
Read on to see what you can do with this list of clusters.
Avro is a remote procedure call and data serialization framework developed within Apache’s Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format. If you’re unfamiliar with Avro, I would highly recommend the explanation of Dennis Vriend at Binx.io about an introduction into Avro.
Over 272 Jira tickets have been resolved, and 844 PRs are included since 1.8.2. I’d like to point out several major changes.
That’s a lot of tickets.
In the 1.7 release, Flink has introduced the concept of temporal tables into its streaming SQL and Table API: parameterized views on append-only tables — or, any table that only allows records to be inserted, never updated or deleted — that are interpreted as a changelog and keep data closely tied to time context, so that it can be interpreted as valid only within a specific period of time. Transforming a stream into a temporal table requires:
– Defining a primary key and a versioning field that can be used to keep track of the changes that happen over time;
– Exposing the stream as a temporal table function that maps each point in time to a static relation.
It looks pretty good.
To avoid this overhead, you must track the idleness of the EMR cluster and terminate it if it is running idle for long hours. There is the Amazon EMR native IsIdle Amazon CloudWatch metric, which determines the idleness of the cluster by checking whether there’s a YARN job running. However, you should consider additional metrics, such as SSH users connected or Presto jobs running, to determine whether the cluster is idle. Also, when you execute any Spark jobs in Apache Zeppelin, the IsIdle metric remains active (1) for long hours, even after the job is finished executing. In such cases, the IsIdle metric is not ideal in deciding the inactivity of a cluster.
In this blog post, we propose a solution to cut down this overhead cost. We implemented a bash script to be installed in the master node of the EMR cluster, and the script is scheduled to run every 5 minutes. The script monitors the clusters and sends a CUSTOM metric EMR-INUSE (0=inactive; 1=active) to CloudWatch every 5 minutes. If CloudWatch receives 0 (inactive) for some predefined set of data points, it triggers an alarm, which in turn executes an AWS Lambda function that terminates the cluster.
We went a slightly different route for auto-termination, killing after a fixed number of hours.
When a NameNode restarts, it must load the filesystem metadata from local disk into memory. This means that if the namenode metadata is large, restarts will be slower. The NameNode must also track changes in the block locations on the cluster. Too many small files can also cause the NameNode to run out of metadata space in memory before the DataNodes run out of data space on disk. The datanodes also report block changes to the NameNode over the network; more blocks means more changes to report over the network.
More files mean more read requests that need to be served by the NameNode, which may end up clogging NameNode’s capacity to do so. This will increase the RPC queue and processing latency, which will then lead to degraded performance and responsiveness. An overall RPC workload of close to 40K~50K RPCs/s is considered high.
There are a few reasons you want to pack data into large files on Hadoop and this explains them well.
At a high level, when you use the Streams DSL, it auto-creates the processor nodes as well as state stores if needed, and connects them to construct the processor topology. To dig a little deeper, let’s take an example and focus on stateful operators in this section.
An important observation regarding the Streams DSL is that most stateful operations are keyed operations (e.g., joins are based on record keys, and aggregations are based on grouped-by keys), and the computation for each key is independent of all the other keys. These computational patterns fall under the term data parallelism in the distributed computing world. The straightforward way to execute data parallelism at scale is to just partition the incoming data streams by key, and work on each partition independently and in parallel. Kafka Streams leans heavily on this technique in order to achieve scalability in a distributed computing environment.
They then use that info to show you how you can make your Streams apps faster.