partitioning | 易学教程

Is Zookeeper a must for Kafka?

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问题 In Kafka, I would like to use only a single broker, single topic and a single partition having one producer and multiple consumers (each consumer getting its own copy of data from the broker). Given this, I do not want the overhead of using Zookeeper; Can I not just use the broker only? Why is a Zookeeper must? 回答1: Yes, Zookeeper is required for running Kafka. From the Kafka Getting Started documentation: Step 2: Start the server Kafka uses zookeeper so you need to first start a zookeeper

Partitioning mySQL tables that has foreign keys?

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问题 What would be an appropriate way to do this, since mySQL obviously doesnt enjoy this. To leave either partitioning or the foreign keys out from the database design would not seem like a good idea to me. I'll guess that there is a workaround for this? Update 03/24: http://opendba.blogspot.com/2008/10/mysql-partitioned-tables-with-trigger.html How to handle foreign key while partitioning Thanks! 回答1: It depends on the extent to which the size of rows in the partitioned table is the reason for

spark read partitioned data in S3 partly in glacier

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问题 I have a dataset in parquet in S3 partitioned by date (dt) with oldest date stored in AWS Glacier to save some money. For instance, we have... s3://my-bucket/my-dataset/dt=2017-07-01/ [in glacier] ... s3://my-bucket/my-dataset/dt=2017-07-09/ [in glacier] s3://my-bucket/my-dataset/dt=2017-07-10/ [not in glacier] ... s3://my-bucket/my-dataset/dt=2017-07-24/ [not in glacier] I want to read this dataset, but only the a subset of date that are not yet in glacier, eg: val from = "2017-07-15" val to

Grouping lists into groups of X items per group

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问题 I'm having a problem knowing the best way to make a method to group a list of items into groups of (for example) no more than 3 items. I've created the method below, but without doing a ToList on the group before I return it, I have a problem with it if the list is enumerated multiple times. The first time it's enumerated is correct, but any additional enumeration is thrown off because the two variables (i and groupKey) appear to be remembered between the iterations. So the questions are: Is

Spark: Order of column arguments in repartition vs partitionBy

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问题 Methods taken into consideration ( Spark 2.2.1 ): DataFrame.repartition (the two implementations that take partitionExprs: Column* parameters) DataFrameWriter.partitionBy Note: This question doesn't ask the difference between these methods From docs of partitionBy : If specified, the output is laid out on the file system similar to Hive 's partitioning scheme . As an example, when we partition a Dataset by year and then month, the directory layout would look like: year=2016/month=01/ year

mysql database automatic partitioning

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I have a mysql database table that I want to partition by date, particularly by month & year. However, when new data is added for a new month, I don't want to need to manually update the database. When I initially create my database, I have data in Nov 09, Dec 09, Jan 10, etc. Now when February starts, I'd like a Feb 10 partition automatically created. Is this possible? Nick Craver There are a few solutions out there, if you want a total solution, check this post out on kickingtyres . It's a basic combination of a stored procedure handling the partition analysis and creation (with some logging

Partitioning data set in r based on multiple classes of observations

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I'm trying to partition a data set that I have in R, 2/3 for training and 1/3 for testing. I have one classification variable, and seven numerical variables. Each observation is classified as either A, B, C, or D. For simplicity's sake, let's say that the classification variable, cl, is A for the first 100 observations, B for observations 101 to 200, C till 300, and D till 400. I'm trying to get a partition that has 2/3 of the observations for each of A, B, C, and D (as opposed to simply getting 2/3 of the observations for the entire data set since it will likely not have equal amounts of each

Spark: save DataFrame partitioned by “virtual” column

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I'm using PySpark to do classic ETL job (load dataset, process it, save it) and want to save my Dataframe as files/directory partitioned by a "virtual" column; what I mean by "virtual" is that I have a column Timestamp which is a string containing an ISO 8601 encoded date, and I'd want to partition by Year / Month / Day; but I don't actually have either a Year, Month or Day column in the DataFrame; I have this Timestamp from which I can derive these columns though, but I don't want my resultat items to have one of these columns serialized. File structure resulting from saving the DataFrame to

MySQL Partitioning / Sharding / Splitting - which way to go?

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We have an InnoDB database that is about 70 GB and we expect it to grow to several hundred GB in the next 2 to 3 years. About 60 % of the data belong to a single table. Currently the database is working quite well as we have a server with 64 GB of RAM, so almost the whole database fits into memory, but we’re concerned about the future when the amount of data will be considerably larger. Right now we’re considering some way of splitting up the tables (especially the one that accounts for the biggest part of the data) and I’m now wondering, what would be the best way to do it. The options I’m

Partition MySQL table by Column Value

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I have a MySQL table with 20 million rows. I want to partition to boost speed. The table is in the following format: column column column sector data data data Capital Goods data data data Transportation data data data Technology data data data Technology data data data Capital Goods data data data Finance data data data Finance I have applied partitions using the following code: ALTER TABLE technical PARTITION BY LIST COLUMNS (sector) ( PARTITION P1 VALUES IN ('Capital Goods'), PARTITION P2 VALUES IN ('Health Care'), PARTITION P3 VALUES IN ('Transportation'), PARTITION P4 VALUES IN ('Finance'