- Dec 2024
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book.originit.top book.originit.top
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coalesce 会降低同一个 stage 计算的并行度，导致 cpu 利用率不高，任务执行时间变长。我们目前有一个实现是需要将最终的结果写成单个 avro 文件，前面的转换过程可能是各种各样的，我们在最后阶段加上 repartition(1).write().format('avro').mode('overwrite').save('path')。最近发现有时前面的转换过程中有排序时，使用 repartition(1) 有时写得单文件顺序不对，使用 coalesce(1) 顺序是对的，但 coalesce(1) 有性能问题。目前想到可以 collect 到 d
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- Dec 2022
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www.zhihu.com www.zhihu.com
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Apache Flink和Apache Spark发展前景分别怎样？
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www.zhihu.com www.zhihu.com
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Spark 千万级用户相似度计算？
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- Sep 2022
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www.smithsonianmag.com www.smithsonianmag.com
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When it comes to understanding our planet and its future, can novelists reach people in ways that scientists cannot?
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- Jun 2022
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community.databricks.com community.databricks.com
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You can use the DataFrame's randomSplit function
split dataframe
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- May 2022
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spark.apache.org spark.apache.org
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Spark properties mainly can be divided into two kinds: one is related to deploy, like “spark.driver.memory”, “spark.executor.instances”, this kind of properties may not be affected when setting programmatically through SparkConf in runtime, or the behavior is depending on which cluster manager and deploy mode you choose, so it would be suggested to set through configuration file or spark-submit command line options; another is mainly related to Spark runtime control, like “spark.task.maxFailures”, this kind of properties can be set in either way.
spark properties
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Local file Local file
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keep only what resonates in a trusted place thatyou control, and to leave the rest aside
Though it may lead down the road to the collector's fallacy, one should note down, annotate, or highlight the things that resonate with them. Similar to Marie Kondo's concept in home organization, one ought to find ideas that "spark joy" or move them internally. These have a reasonable ability to be reused or turned into something with a bit of coaxing and work. Collect now to be able to filter later.
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community.cloudera.com community.cloudera.com
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job.local.dir
to save data by spark
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- Apr 2022
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www2.le.ac.uk www2.le.ac.uk
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E-tivities generally involve the tutor providing a small piece of information, stimulus or challenge, which Salmon refers to as the 'spark'.
Efetivamente estas e-atividades são mesmo isso, um estimulo importante neste novo ensino. Os alunos precisam de se sentir parte da "sala de aula" e de se sentirem motivados à aprendizagem.
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- Jan 2022
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www.apartmenttherapy.com www.apartmenttherapy.com
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https://www.apartmenttherapy.com/marie-kondo-tokimeku-spark-joy-translation-266496
on the translation of tokimeku, or ときめく, as "spark joy"
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- May 2021
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spark.apache.org spark.apache.org
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local[N]
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- Apr 2021
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datamechanics.co datamechanics.co
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With Spark 3.1, the Spark-on-Kubernetes project is now considered Generally Available and Production-Ready.
With Spark 3.1 k8s becomes the right option to replace YARN
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- Feb 2021
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itnext.io itnext.io
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Consider the amount of data and the speed of the data, if low latency is your priority use Akka Streams, if you have huge amounts of data use Spark, Flink or GCP DataFlow.
For low latency = Akka Streams
For huge amounts of data = Spark, Flink or GCP DataFlow
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Drink a cup of caffeinated beverage beforehand
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- Jun 2020
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ceur-ws.org ceur-ws.org
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- Sep 2019
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jaceklaskowski.gitbooks.io jaceklaskowski.gitbooks.io
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blog.minio.io blog.minio.io
- Mar 2019
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testdriven.io testdriven.io
- Dec 2018
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docs.aws.amazon.com docs.aws.amazon.com
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aws.amazon.com aws.amazon.com
- Nov 2018
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elearningindustry.com elearningindustry.com
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Top 10 Tools For The Digital Classroom
This article presents a variety of new tools and apps that will enhance the digital classroom experience. Some of the new tools mentioned are Socrative, Scratch, Prezi, Google classroom and more!
Excellent list to get your digital room started!
RATING: 5/5 (rating based upon a score system 1 to 5, 1= lowest 5=highest in terms of content, veracity, easiness of use etc.)
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- Oct 2018
- Jan 2018
- May 2017
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blog.thehumangeo.com blog.thehumangeo.com
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- Apr 2017
- Apr 2014
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www.dbms2.com www.dbms2.com
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Mike Olson of Cloudera is on record as predicting that Spark will be the replacement for Hadoop MapReduce. Just about everybody seems to agree, except perhaps for Hortonworks folks betting on the more limited and less mature Tez. Spark’s biggest technical advantages as a general data processing engine are probably: The Directed Acyclic Graph processing model. (Any serious MapReduce-replacement contender will probably echo that aspect.) A rich set of programming primitives in connection with that model. Support also for highly-iterative processing, of the kind found in machine learning. Flexible in-memory data structures, namely the RDDs (Resilient Distributed Datasets). A clever approach to fault-tolerance.
Spark's advantages:
- DAG processing model
- programming primitives for DAG model
- highly-iterative processing suited for ML
- RDD in-memory data structures
- clever approach to fault-tolerance
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