partitioning | 易学教程

How to handle foreign key while partitioning

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问题 I am working on fleet management. I am having large amount of writes on a location table with following columns date time vehicle no. long latitude speed userid (which is foreign key...) Here this table is going to have write operation every 3 sec. Hence there will be millions of record in it. So to retrieve faster data I AM PLANNING Partition. Now my question:- How to handle foreign key? I heard that partition does not support foreign key Which column should be used for partition. is it

How to update partition metadata in Hive , when partition data is manualy deleted from HDFS

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问题 What is the way to automatically update the metadata of Hive partitioned tables? If new partition data's were added to HDFS (without alter table add partition command execution) . then we can sync up the metadata by executing the command 'msck repair'. What to be done if a lot of partitioned data were deleted from HDFS (without the execution of alter table drop partition commad execution). What is the way to syncup the Hive metatdata? 回答1: EDIT : Starting with Hive 3.0.0 MSCK can now discover

How does partitioning work in Spark?

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I'm trying to understand how partitioning is done in Apache Spark. Can you guys help please? Here is the scenario: a master and two nodes with 1 core each a file count.txt of 10 MB in size How many partitions does the following create? rdd = sc.textFile(count.txt) Does the size of the file have any impact on the number of partitions? mrmcgreg By default a partition is created for each HDFS partition, which by default is 64MB (from the Spark Programming Guide ). It's possible to pass another parameter defaultMinPartitions which overrides the minimum number of partitions that spark will create.

Apache Spark: Get number of records per partition

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问题 I want to check how can we get information about each partition such as total no. of records in each partition on driver side when Spark job is submitted with deploy mode as a yarn cluster in order to log or print on the console. 回答1: You can get the number of records per partition like this : df .rdd .mapPartitionsWithIndex{case (i,rows) => Iterator((i,rows.size))} .toDF("partition_number","number_of_records") .show But this will also launch a Spark Job by itself (because the file must be

How to partition Mysql across MULTIPLE SERVERS?

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问题 I know that horizontal partitioning...you can create many tables. How can you do this with multiple servers? This will allow Mysql to scale. Create X tables on X servers? Does anyone care to explain, or have a good beginner's tutorial (step-by-step) that teaches you how to partition across multiple servers? 回答1: With MySQL, people generally do what is called application based sharding . In a nutshell, you will have the same database structure on multiple database servers. But it won't contain

Partitioning a large skewed dataset in S3 with Spark's partitionBy method

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问题 I am trying to write out a large partitioned dataset to disk with Spark and the partitionBy algorithm is struggling with both of the approaches I've tried. The partitions are heavily skewed - some of the partitions are massive and others are tiny. Problem #1 : When I use repartition before repartitionBy , Spark writes all partitions as a single file, even the huge ones val df = spark.read.parquet("some_data_lake") df .repartition('some_col).write.partitionBy("some_col") .parquet("partitioned

Table partitioning using 2 columns

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问题 Is it possible to partition a table using 2 columns instead of only 1 for the partition function? Consider a table with 3 columns ID (int, primary key, Date (datetime), Num (int) I want to partition this table by 2 columns: Date and Num . This is what I do to partition a table using 1 column (date): create PARTITION FUNCTION PFN_MonthRange (datetime) AS RANGE left FOR VALUES ('2009-11-30 23:59:59:997', '2009-12-31 23:59:59:997', '2010-01-31 23:59:59:997', '2010-28-02 23:59:59:997', '2010-03

How to partition an array of integers in a way that minimizes the maximum of the sum of each partition?

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The inputs are an array A of positive or null integers and another integer K. We should partition A into K blocks of consecutive elements (by "partition" I mean that every element of A belongs to some block and 2 different blocks don't contain any element in common). We define the sum of a block as sum of the elements of the block. The goal is to find such a partition in K blocks such that the maximum of the sums of each block (let's call that " MaxSumBlock ") is minimized. We need to output the MaxSumBlock (we don't need to find an actual partition) Here is an example: Input: A = {2, 1, 5, 1,

Split a list of numbers into n chunks such that the chunks have (close to) equal sums and keep the original order

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This is not the standard partitioning problem, as I need to maintain the order of elements in the list. So for example if I have a list [1, 6, 2, 3, 4, 1, 7, 6, 4] and I want two chunks, then the split should give [[1, 6, 2, 3, 4, 1], [7, 6, 4]] for a sum of 17 on each side. For three chunks the result would be [[1, 6, 2, 3], [4, 1, 7], [6, 4]] for sums of 12, 12, and 10. Edit for additional explanation I currently divide the sum with the number of chunks and use that as a target, then iterate till I get close to that target. The problem is that certain data sets can mess the algorithm up, for

Spark SQL saveAsTable is not compatible with Hive when partition is specified

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问题 Kind of edge case, when saving parquet table in Spark SQL with partition, #schema definitioin final StructType schema = DataTypes.createStructType(Arrays.asList( DataTypes.createStructField("time", DataTypes.StringType, true), DataTypes.createStructField("accountId", DataTypes.StringType, true), ... DataFrame df = hiveContext.read().schema(schema).json(stringJavaRDD); df.coalesce(1) .write() .mode(SaveMode.Append) .format("parquet") .partitionBy("year") .saveAsTable("tblclick8partitioned");