High Performance Spark: Best Practices For Scal... BEST
This post discussed EMR Managed Scaling, which automatically resizes your cluster for best performance at the lowest possible cost. For more information, see Using EMR Managed Scaling in Amazon EMR, or view the EMR Managed Scaling demo:
High Performance Spark: Best Practices for Scal...
App developers start to consider scaling out or horizontal scaling when they can't get enough resources for their workloads, even operating on the highest performance levels. With horizontal scaling, data is split into several databases, or shards, across servers, and each shard can be scaled up or down independently.
Traditional coarse-grained autoscaling algorithms do not fully scale down cluster resources allocated to a Spark job while the job is running. The main reason is the lack of information on executor usage. Removing workers with active tasks or in-use shuffle files would trigger re-attempts and recomputation of intermediate data, which leads to poorer performance, lower effective utilization and therefore higher costs for the user. However in cases where there are only a few active tasks running on a cluster, such as when the Spark job exhibits skew or when a particular stage of the job has lower resource requirements, the inability to scale down leads to poorer utilization and therefore higher costs for users. This is a massive missed opportunity for traditional autoscaling.
If you enable DynamoDB auto scaling for a table that has one or more global secondary indexes, we highly recommend that you also apply auto scaling uniformly to those indexes. This will help ensure better performance for table writes and reads, and help avoid throttling. You can enable auto scaling by selecting Apply same settings to global secondary indexes in the AWS Management Console. For more information, see Enabling DynamoDB auto scaling on existing tables.
Recently, the huge amounts of data and its incremental increase have changed the importance of information security and data analysis systems for Big Data. Intrusion detection system (IDS) is a system that monitors and analyzes data to detect any intrusion in the system or network. High volume, variety and high speed of data generated in the network have made the data analysis process to detect attacks by traditional techniques very difficult. Big Data techniques are used in IDS to deal with Big Data for accurate and efficient data analysis process. This paper introduced Spark-Chi-SVM model for intrusion detection. In this model, we have used ChiSqSelector for feature selection, and built an intrusion detection model by using support vector machine (SVM) classifier on Apache Spark Big Data platform. We used KDD99 to train and test the model. In the experiment, we introduced a comparison between Chi-SVM classifier and Chi-Logistic Regression classifier. The results of the experiment showed that Spark-Chi-SVM model has high performance, reduces the training time and is efficient for Big Data.
The researchers are still seeking to find an effective way to detect the intrusions with high performance, high speed and a low of false positive alarms rate. The main objective of this paper is to improve the performance and speed of intrusion detection within Big Data environment. In this method, the researchers used Apache Spark Big Data tools because it is 100 times faster than Hadoop , the feature selection that takes the amount of computation time, and this time can be reduced when using SVM on KDD datasets . Therefore, we used SVM algorithm with Chi-squared for feature selection and compared it with Logistic Regression classifier based on area under curve (ROC), Area Under Precision Recall Curve and time metrics.
SVM works by maximizing the margin to obtain the minimized error classification and best performance with the maximal margin between the vectors of the two classes that are named maximum margin classifier, showing in Fig. 4. The following equation is used to find the optimal separating hyperplane of a linear classification:
In this paper, the researchers introduced Spark-Chi-SVM model for intrusion detection that can deal with Big Data. The proposed model used Spark Big Data platform which can process and analyze data with high speed. Big data have a high dimensionality that makes the classification process more complex and takes a long time. Therefore, in the proposed model, the researchers used ChiSqSelector to select related features and SVMWithSGD to classify data into normal or attack. The results of the experiment showed that the model has high performance and speed. In future work, the researchers can extend the model to a multi-classes model that could detect types of attack.
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However, HBase has many features which supports both linear and modular scaling.HBase clusters expand by adding RegionServers that are hosted on commodity class servers.If a cluster expands from 10 to 20 RegionServers, for example, it doubles both in terms of storage and as well as processing capacity.An RDBMS can scale well, but only up to a point - specifically, the size of a single databaseserver - and for the best performance requires specialized hardware and storage devices.HBase features of note are:
AdvancedScanResultConsumer executes callbacks inside the framework thread. It is not allowed to do time consuming work in the callbacks else it will likely block the framework threads and cause very bad performance impact. As its name, it is designed for advanced users who want to write high performance code. See org.apache.hadoop.hbase.client.example.HttpProxyExample for how to write fully asynchronous code with it.
Small- and mid-sized companies most often use vertical scaling for their applications because it allows businesses to scale relatively quickly compared to using horizontal scaling. One drawback of vertical scaling is that it poses a higher risk for downtime and outages than horizontal scaling. Correctly provisioning your resources is the best way to ensure that upgrading was worth it and that your business will not experience the negative effects of vertical scaling.
Copy On Write - This storage type enables clients to ingest data on columnar file formats, currently parquet. Any new data that is written to the Hudi dataset using COW storage type, will write new parquet files. Updating an existing set of rows will result in a rewrite of the entire parquet files that collectively contain the affected rows being updated. Hence, all writes to such datasets are limited by parquet writing performance, the larger the parquet file, the higher is the time taken to ingest the data.
Merge On Read - This storage type enables clients to ingest data quickly onto row based data format such as avro. Any new data that is written to the Hudi dataset using MOR table type, will write new log/delta files that internally store the data as avro encoded bytes. A compaction process (configured as inline or asynchronous) will convert log file format to columnar file format (parquet). Two different InputFormats expose 2 different views of this data, Read Optimized view exposes columnar parquet reading performance while Realtime View exposes columnar and/or log reading performance respectively. Updating an existing set of rows will result in either a) a companion log/delta file for an existing base parquet file generated from a previous compaction or b) an update written to a log/delta file in case no compaction ever happened for it. Hence, all writes to such datasets are limited by avro/log file writing performance, much faster than parquet. Although, there is a higher cost to pay to read log/delta files vs columnar (parquet) files. 041b061a72