Queiroz, and R. Allen, M.
Trial Storm Applied: Strategies for real-time event processing Ebook
Jankowski, and P. Liu, A. Dastjerdi, and R. Centenaro, L. Vangelista, A. Zanella, and M. Dean and S. Chen, S. Alspaugh, D. Borthakur, and R. Babcock, S. Babu, M. Datar, R. Motwani, J.
Widom et al. Kulkarni, N. Bhagat, M. Fu, V. Kedigehalli, C. Kellogg et al. Gedik, H. Ozsema, and O. Tang and B. Sattler and F. Wu, Y. Diao, and S. Gyllstrom, E. Wu, H. Chae, Y. Diao, P. Stahlberg et al. He, M. Yang, Z. Guo, R. Chen, B. Su et al. Sajjad, K. Danniswara, A.
Al-shishtawy, and V. Chan , Apache quarks, watson, and streaming analytics: Saving the world, one smart sprinkler at a time , Bluemix Blog , Hirzel, S. Schneider, and B. Netto, C. Cardonha, R. Cunha, and M. Tolosana-calasanz, J. Baares, C. Pham, and O. Rana , Resource management for bursty streams on multi-tenancy cloud environments , Future Generation Computer Systems , vol. Chen, D. Dewitt, F. Tian, and Y. Arasu, B. Babu, J. Cieslewicz, M. Datar et al. Babu, R. Motwani, and M. Abadi, D. Carney, U. Cherniack, C. Convey et al.
Balazinska, H. Balakrishnan, and M. Tatbul, U. Abadi, Y. Ahmad, M. Balazinska, U. Cetintemel, M. Cherniack et al. Basanta-val, N. Fernndez-garca, A. Wellings, and N. Audsley , Improving the predictability of distributed stream processors , special Section: Cloud Computing: Security, Privacy and Practice.
Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. Joseph et al. Neumeyer, B. Robbins, A. Nair, and A. Vavilapalli, A. Murthy, C. Douglas, S. Agarwal, M. Konar et al. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma et al. Zaharia, T. Das, H. Li, T. Hunter, S. Shenker et al. Shah, J. Hellerstein, S.
TOP APACHE STORM BOOKS
Chandrasekaran, and M. Spark — Spark consists of Java and Scala APIs with practical programming which makes topology code somewhat difficult to understand. But as in, API documentation and samples are easily available for the developers, it becomes easy. Ease of Operability Storm — The installation and deployment of Storm is somewhat tricky. It remains dependent on zookeeper cluster to coordinate with states, clusters, and statistics.
Spark — Spark itself is the basic framework for the execution of Spark Streaming. It is required to enable checkpointing to make application drivers fault-tolerant which makes Spark dependent on HDFS i. Low Latency Storm: Apache Storm provides better latency with little constraints. Spark: Apache Spark provides higher latency as compared to Apache Storm A certification is a credential that helps you stand out of the crowd. Development Cost Storm: In Apache Storm, it is not possible to use same code base for both the stream processing and batch processing.
Spark: In Apache spark, it is possible to use same code base for both the stream processing as well as batch processing. At least Once Tuples are processed at least once but can be processed more than once Exactly Once Tuples are processed at least once Apache Spark supports only one processing mode. Exactly Once Throughput 10k records per node per second k records per node per second Development Cost In Apache Storm, it is not allowed to apply same code for stream processing and batch processing. To get over from driver failure, Spark streaming uses data checkpointing Final Words: Apache Storm Vs Apache Spark The study of Apache Storm Vs Apache Spark concludes that both of these offer their application master and best solutions to solve transformation problem and streaming ingestion.
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Our Products. Please wait Apache Storm. Apache Spark. Apache Storm supports micro-batch processing. Apache Spark supports batch processing. Spout is the source of stream processing in Storm. HDFS is the source of stream processing in Spark.
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Programming Languages. Java, Scala, and Clojure Scala supports multiple languages. Java, Scala Scala supports fewer languages. Mesos and Yarn are responsible for resource management. Meson and Yarn are responsible for resource management. Apache Storm supports state management.
Apache Spark also supports state management. Basic monitoring using Ganglia. Finally, no published messages for a configurable time interval. Messages by worker processes are affected by the failure of Nimbus or a producer are appended to a topic partition in the order they are Supervisor. Within Storm, decrease of performance during execution. It is due to the accu- ZooKeeper is used to achieve coordination between Nimbus mulation of erroneous conditions in the system state or to the and Supervisor nodes, and to monitor their states. These bugs are worker processes, executors and tasks .
Each topology can in- usually subtle or expensive to expose and remove during testing clude several worker processes. A worker process runs one or more components of a topology i. Each topology and debugging, as their manifestation may require a long time . Each computational node can includes one or aging, including: web servers [5,22], operating systems , web more worker processes. These three parameters can be configured applications , the Java Virtual Machine , data base man- in order to obtain a defined parallelism degree.
Finally, the number agement systems , cloud computing  and virtualization of executors and of worker processes can be changed run-time to environments , data centers . Software aging effects can be detected by means of aging indi- Internal queue-based messaging mechanisms enable commu- cators , typically, system variables that can be directly measured nication among executors within a worker process intra-worker and related to aging.
Examples are: system resources usage, such communication , as well as among worker processes belonging to as free physical memory, used swap space, file and process tables the same topology inter-worker communication. Each executor size, or user-perceived performance indicators, like response time has its own incoming and outgoing queues.
Many studies address the problems of the detection In order to achieve an inter-topology communication, external and of the prediction of the Time To Aging Failure TTAF , within messaging system, such as Kafka or RabbitMQ, must be adopted, which a preventive action should be taken. The main strategies to as shown in Fig. Storm topology communication. As for how to rejuvenate, application- parameters; measurement-based, where observed field data are specific actions can be applied, i. However, they can be less ef- fective, since they have some simplifying assumptions, such as 4.
Stream processing test application the one that the distributions characterizing the system behavior e. Measurement- 4. Overview based studies forecast software aging based on direct measure- ments e. Their ad- signed to be simple and realistic. Simple means that the appli- vantage is that software aging forecasting can adapt to the current cation does neither perform complex processing, nor uses any sophisticated external components.
This reduces the chance of condition of the system e. Moreover, predict the occurrence of aging phenomena. On the other hand, the application itself has been tested in order to increase the measurement-based approaches may be not easily generalizable confidence in its correctness. This is a similar approach as in past to other systems, since they exploit aspects related to the nature studies . Realistic means that the application processes a real of the considered system. Hybrid models try to combine both workload, rather than a synthetic one.
The application Fig. The design choice of having each runtime proactive fault tolerance technique named software reju- processing step implemented by a topology is common among venation. Rejuvenation was defined in  as the preemptive rollback developers of stream processing topologies. In fact, introducing a of continuously running applications to prevent failures in the future.
For the purpose of this study, it also simplifies even the very fact of their existence. The objective of rejuvenation the detection and diagnosis of a possible aging phenomenon. The is to avoid — or at least postpone — aging-related failures, and next subsections provide a description of each topology. It has to be applied carefully, assuring that the downtime cost due to rejuvenation 4. Feed stream topology during which an application may be unavailable is lower than the cost of unscheduled downtime due to failures that would occur The first component is the wikipedia-feed-stream- otherwise.
It connects the application to Rejuvenation strategies aim at determining when and how the Wikipedia Internet Relay Chat IRC server at the address to perform rejuvenation. The software aging analysis techniques irc. This Please cite this article in press as: M. The wikipedia-feed-stats-topology component. The experimental application. Feed statistics topology The last topology provides statistics on the parsed mes- sages, computed by gathering them in temporal windows of 10 s.
This is accomplished by the tumbling window bolt feed- StatsCalculatorBolt Fig. The wikipedia-feed-stream-topology component. As feedStatsCalcula- torBolt can be configured with more tasks, incoming messages might be distributed among tasks in a wrong way: if the stream grouping is not chosen appropriately, messages belonging to the same channels e.
For this reason, the component feedStatsPreprocessorBolt has been con- nected to the feedStatsCalculatorBolt to provides messages to its tasks in the proper way, by using fields grouping. The last bolt feedStatsWriterKafkaBolt writes the statistics in a topic named wikipedia-feed-stats. The wikipedia-feed-parse-topology component. Experiments 5. Storm calls the nextTuple 3 hard disk. Initially, the system is reset and a fresh installation of method of the feedStreamerSpout to pop a message out of the the debian This bolt and auxiliary software are not activated.
In this way, it is ensured writes the message in a Kafka topic named wikipedia-feed- that there are no useless services running in background. The raw-data, so that it can be processed by the next topology. The measures of interest related to named message replication factor MRF. Feed parsing topology 5. Experiments, metrics, analysis method The second topology Fig. The first two aim at investigating the possible presence of rics, both at global level Table 1 and at process level Table 2 , software aging under a real unaltered workload Experiment 1, which are indirect indicators, as their trends do not necessarily Section 5.
They are useful to explain stress factor Experiment 2, Section 5. The second two exper- the aging dynamics, and to identify the main contributors to the iments Section 5. Differently from conventional controlled experiments in soft- We point out explicitly that Experiment 1 uses a real workload, ware aging studies, we need to consider that the workload is as input data come dynamically from the actual Wikipedia IRC real and variable — only the replication factor is controlled in server channels, while experiments 2—4 are based on a replication experiments 2—4.
Workload-dependent analysis requires not only of real data which preserves the pattern of requests, while mim- to compute the trend of the indicators, but also to relate them to the icking the presence of more users. Indeed, increase of memory consumption or decrease of The main aging indicators we consider regard both the user- throughput may well be due to the load increase, in which case the perceived performance in terms of throughput and latency and performance degradation is not a symptom of software aging — it is the resource depletion in terms of real memory consumption.
Let us consider one summary where TM is the total memory. The page cache contains a copy of indicator for memory consumption MC , one for throughput TT , recently accessed files in kernel memory. If the memory not allocated by the kernel or user processes, its memory MKT does not detect any trend for at least one of these indicators consumption is quite large and would bias the analysis; therefore, in the current observation window, the window is expanded to it is subtracted from total memory.
Buffers also stores temporary consider more samples. When the MKT succeeds for at least one data which can be freed if needed, hence it is subtracted, too. An variable, the window slides over; the new window starts from the increasing trend of the above metric over time is useful to detect first sample just after the previous window. The effectiveness of the Mann—Kendall test for aging detection The performance indicator is the topology throughput TT , has been investigated in : Machida et al.
It is the percentage of the incoming tuples have is possible for it to indicate software aging even where there is been successfully processed by the topology in a certain time no aging. This limitation of MKT can be contrasted by increasing interval. It is measured separately for the three topologies.
The the amount of data considered in the test, at the cost of increasing incoming request rate is the number of emitted tuples per sec- the time to detect aging. Despite its limit, MKT remains the most ond taken at the first topology, namely the wikipedia-feed- widely adopted test to detect aging. However, the choice of the stream-topology.
Storm Applied: Strategies for real-time event processing - Free PDF Download
Then, the throughput is measured in all the observation window is crucial, and it has to be performed carefully. We consider this behavior a potential related to memory leakage. The large 5. The message replica- have an aging trend, the likelihood that there is an actual tion factor is set to 1 — i. The workload occurring during the experimental period is showed in Fig. All the other cases do not give clues about possible aging i. A workload- there is no aging in that window.
We count how many times, in independent analysis — i. This approach allows entire time series — highlights a trend on memory consumption 2. At latency level, there is a very slight trend for the suppose that the MKT test is applied over 50 time windows across feed-stream topology amounting to 1. Consider the case of the MC aging indicator. The per process analysis will highlight if this is some- AND the MKT applied to the input WL series, in the same time how associated with the memory consumption at process level. Table 3 lists the results of the workload-dependent correspon- windows, notifies a decreasing or no trend, we count a potential dence analysis: it shows the percentage of time the aging indi- aging behavior.
The global Please cite this article in press as: M. The input workload, test 1. Global memory consumption, test 1. Throughput of wikipedia-feed-stream-topology, test 1. Y -axis scale: 0— Table 5 summa- despite there are windows where latency increases unexpectedly, rizes the results. The other indicator with a relatively high of the feedstream and feed-parse topologies to the memory percentage is for memory consumption, whose trend is increasing consumption these had also the trend on Latency. Considering in The global trend for MC is 2. Kafka and Nimbus. There is a slight trend in the disk writing operations, whereas the CPU- and memory-related indicators confirm the stable behavior of Storm under the considered load.
Experiment 2 RQ2 5. Process-level analysis The second experiment addresses research question RQ2, inves- We analyze now the direct aging indicators for each process tigating if the aging behavior changes under a workload by an order over the entire time series. Then, the relation of the process aging of magnitude heavier. The replication factor is set to Throughput of wikipedia-feed-parse-topology, test 1 Y -axis scale: 98— Throughput of wikipedia-feed-stats-topology, test 1 Y -axis scale: 99— Latency per request of wikipedia-feed-stream-topology, test 1.
Global-level analysis Figs. A workload- The workload during the experimental period is shown in independent analysis — of a global trend over the entire time series — highlights a remarkable trend in the memory consumption, Fig. Latency per request of wikipedia-feed-parse-topology, test 1.
Latency per request of wikipedia-feed-stats-topology, test 1. Table 3 Correspondence analysis between workload and aging indicators, test 1. Percentage of occurrence of trends in the aging indicator row in correspondence to the WL trend column. Moreover, in the windows where such an Please cite this article in press as: M.
The input workload, test 2. Global memory consumption, test 2. Throughput of wikipedia-feed-stream-topology. Y -axis scale: 90— Process-level analysis Table 8 summarizes the results at process level, considering in most cases a non-increasing trend namely, what we defined an both the aging indicators over the entire time series, and then the aging situation. The indicators highlight a positive significant stats-topology.
This is in line with the latency result, suggest- trends on disk activity writing activity , as well as the contribution ing the wikipedia-feed-parse-topology as a bottleneck. If we consider the workload-dependent analysis, the percentages of to memory trend given by the cache and buffers.
There seems to be windows where there is an aging trend along with a non-increasing no stress at CPU level. Throughput of wikipedia-feed-parse-topology.