Pyspark Read Parquet With Schema

However, to read NoSQL data that was written to a table in another way, you first need to define the table schema. If we are using earlier Spark versions, we have to use HiveContext which is. I have a file customer. For example, you can read and write Parquet files using Pig and MapReduce jobs. They are extracted from open source Python projects. PySpark Dataframe Sources. 1 employs Spark SQL's built-in functions to allow you to consume data from many sources and formats (JSON, Parquet, NoSQL), and easily perform transformations and interchange between these data formats (structured, semi-structured, and unstructured data). Please, do not be confused, protobuf is a serialization library, but here it's used only to define record with schema. Spark SQL provides support for both reading and writing parquet files that automatically capture the schema of the original data, so there is really no reason not to use Parquet when employing Spark SQL. functions import broadcast sqlContext = SQLContext(sc) df_tiny = sqlContext. As it turns out, real-time data streaming is one of Spark's greatest strengths. def parquet (self, path): """Loads a Parquet file stream, returning the result as a :class:`DataFrame`. Something we've only begun to touch on so far is the benefit of utilizing Apache Spark is larger-scale data pipelines. Apache Spark is written in Scala programming language. pyspark: TypeError: IntegerType can not accept object in type ; apache spark - Can not infer schema for type: when converted RDD to DataFrame; apache spark sql - Read parquet file having mixed data type in a column. The CDH software stack lets you use the tool of your choice with the Parquet file format, for each phase of data processing. Row object while ensuring schema HelloWorldSchema compliance (shape, type and is-nullable condition are tested). Tutorial: Access Data Lake Storage Gen2 data with Azure Databricks using Spark. 多个文件路径用逗号’,’隔开就可以读,比如read. 这里介绍Parquet,下一节会介绍JDBC数据库连接。 Parquet是一种流行的列式存储格式,可以高效地存储具有嵌套字段的记录。Parquet是语言无关的,而且不与任何一种数据处理框架绑定在一起,适配多种语言和组件,能够与Parquet配合的组件有:. Learn how to read and save to CSV a Parquet compressed file with a lot of nested tables and Array types. This post is about analyzing the Youtube dataset using pyspark dataframes. 5 in order to run Hue 3. To write data in parquet we need to define a schema. Internally, Spark SQL uses this extra information to perform extra optimization. Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array. Apache Parquet is a popular columnar storage format which stores its data as a bunch of files. Python is capable of reading Parquet files into Pandas DataFrame using the package ‘fast-parquet’, and with a bit of helper code you can automate the download and reading of Parquet files into a Python command that allows reading the full state of a Salesforce Object into a Pandas DataFrame. It can also be used from pure Python code. DataFrameto HDFS and read it back later on, to save data between sessions, or to cache the result of some preprocessing. The mapping between Avro and Parquet schema. from pyspark import SparkContext, SparkConf // read in text file and split each document into words JavaRDD tokenized = sc. Rather than creating Parquet schema and using ParquetWriter and ParquetReader to write and read file respectively it is more convenient to use a framework like Avro to create schema. Compression. Partitioning This library allows you to easily read and write partitioned data without any extra configuration. param schema: a :class:`pyspark. When reading CSV files with a user-specified schema, it is possible that the actual data in the files does not match the specified schema. I am converting JSON to parquet file conversion using df. 注意:可以读一个parquet文件,也可以读多个parquet文件,select可以用于节约载入内存消耗,也可以让后续dataframe. You can set the following Parquet-specific option(s) for reading Parquet files: * ``mergeSchema``: sets whether we should merge schemas collected from all \ Parquet part-files. according either an avro or parquet schema. ) the 253 L{SchemaRDD} is not operated on directly, as it's underlying 254. Parquet files are self-describing so the schema is preserved. They are extracted from open source Python projects. sql import SparkSession • >>> spark = SparkSession\. param schema: a :class:`pyspark. Using Avro to define schema. Spark SQL is a component on top of Spark Core that introduces a new data abstraction called SchemaRDD, which provides support for structured and semi. The following example demonstrates how to read a Parquet file in a MapReduce job; portions of code specific to Parquet are shown. You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://). 03/11/2019; 7 minutes to read +6; In this article. I am now using the same file name, not a different one. appName("PySpark. Simply running sqlContext. 2) The problem here rises when you have parquet files with different schema and force the schema during read. (Edit 10/8/2015 : A lot has changed in the last few months – you may want to check out my new post on Spark, Parquet & S3 which details some of the changes). With the emergence of new technologies that make data processing lightening fast, and cloud ecosystems which allow for flexibility, cost savings, security, and convenience, there appear to be some…. In the shell you can print schema using printSchema method:. parquet") I got the following error. In this example, the select API is used explicitly to select the fields of the file. It has support for different compression and encoding schemes to. Please, do not be confused, protobuf is a serialization library, but here it’s used only to define record with schema. In Memory In Server Big Data Small to modest data Interactive or batch work Might have many thousands of jobs Excel, R, SAS, Stata,. We are going to load this data, which is in a CSV format, into a DataFrame and then we. find the most popular…. When Avro data is read, the schema used when writing it is always present. def json (self, path, schema = None): """ Loads a JSON file (one object per line) or an RDD of Strings storing JSON objects (one object per record) and returns the result as a :class`DataFrame`. There are a few built-in sources. 4 and Spark 1. PySpark can be launched directly from the command line for interactive use. 5 in order to run Hue 3. Reading with Hive a Parquet dataset written by Pig (and vice versa) leads to various issues, most being related to complex types. Supported file formats are text, csv, json, orc, parquet. Spark SQL can automatically infer the schema of a JSON dataset, and use it to load data into a DataFrame object. Here we have taken the FIFA World Cup Players Dataset. This work is fully open source (Apache-2. Pyspark Read Parquet With Schema. ORC format was introduced in Hive version 0. MEMORY_ONLY_SER): """Sets the storage level to persist its values across operations after the first time it is computed. This is presumably an artifact of Java/Scala, as our Python code is translated into Java jobs. Port details: spark Fast big data processing engine 2. Using Apache Spark on an EMR cluster, I have read in xml data, inferred the schema, and stored it on s3 in parquet format. My spark program has to read from a directory, This directory has data of different schema Dir/subdir1/files 1,10, Alien 1,11, Bob Dir/subdir2/files 2,blue, 123, chicago 2,red, 34,. PySpark SQL CHEAT SHEET FURTHERMORE: Spark, Scala and Python Training Training Course • >>> from pyspark. This conversion can be done using SQLContext. You can even join data from different data sources. parquetDF = spark. Parquet tables created by Impala can be accessed by Hive, and vice versa. They all have better compression and encoding with improved read performance at the cost of slower writes. from pyspark. The following are code examples for showing how to use pyspark. JavaBeans and Scala case classes representing. To support Python with Spark, Apache Spark community released a tool, PySpark. Apache Spark is written in Scala programming language. Therefore, roundtrip in reading and writing XML files has the same structure but writing a DataFrame read from other sources is possible to have a different structure. Another benefit is that since all data in a given column is the same datatype (obviously), compression quality is far superior. csv file into pyspark dataframes ?" -- there are many ways to do this; the simplest would be to start up pyspark with Databrick's spark-csv module. This is different than the default Parquet lookup behavior of Impala and Hive. parquet function that returns an RDD of JSON strings using the column names and schema to. Note that the main difference is that I am attempting to rewrite the file. Using Apache Spark on an EMR cluster, I have read in xml data, inferred the schema, and stored it on s3 in parquet format. write_schema (columns) ¶ Write the dataset schema into the dataset JSON definition file. org/jira/browse/SPARK-16975 which describes a similar problem but with column names. This can only be used to assign a new storage level if the RDD does not have a storage level set yet. Reading Parquet Files in MapReduce. Compression. Parquet is a famous file format used with several tools such as Spark. PySpark SQL Cheat Sheet. Parquet stores nested data structures in a flat columnar format. 多个文件路径用逗号’,’隔开就可以读,比如read. A DataFrame can be constructed from an array of different sources such as Hive tables, Structured Data files, external. What gives? Works with master='local', but fails with my cluster is specified. DataFrame we write it out to a parquet storage. Apache Parquet is a popular columnar storage format which stores its data as a bunch of files. This is the only time a user needs to define a schema since Petastorm translates it into all supported framework formats, such as PySpark, Tensorflow, and pure Python. read and/or session. to_avro() and from_avro() The Avro package provides function to_avro to encode a column as binary in Avro format, and from_avro() to decode Avro binary data into a column. To write data in parquet we need to define a schema. I wrote the following codes. Avro example in hive. MEMORY_ONLY_SER): """Sets the storage level to persist its values across operations after the first time it is computed. This can be used to indicate the type of columns if we cannot infer it automatically. This will override ``spark. ) the 253 L{SchemaRDD} is not operated on directly, as it's underlying 254. For example, you can read and write Parquet files using Pig and MapReduce jobs. Please rescue. But what happens when I rewrite the file with a new schema. wholeTextFiles => file, 내용리턴) md = sc. Apache Parquet is a popular columnar storage format which stores its data as a bunch of files. textFile() method, with the help of Java and Python examples. Generate Schema. Automatic schema conversion Supports most conversions between Spark SQL and Avro records, making Avro a first-class citizen in Spark. I save a Dataframe using partitionBy ("column x") as a parquet format to some path on each worker. appName("Chicago_crime_analysis"). For example, a field containing name of the city will not parse as an integer. The consequences depend on the mode that the parser runs in:. By default, we select smaller physical types in our output Parquet file for certain columns because they only contain small values that fit in smaller types than what the schema would suggest. DataFrameto HDFS and read it back later on, to save data between sessions, or to cache the result of some preprocessing. Pyspark: Parse a column of json strings How to handle changing parquet schema in Apache Spark Reading CSV into a Spark Dataframe with timestamp and date types. 连接本地spark 2. engine, interfaces Python commands with a Java/Scala execution core, and thereby gives Python programmers access to the Parquet format. spark write parquet file. SQLContext (sparkContext, sqlContext=None) [source] ¶. df reads in a dataset from a data source as a DataFrame. appName("PySpark. 2) The problem here rises when you have parquet files with different schema and force the schema during read. Dataframe Creation. Spark SQL is a Spark module for structured data processing. ) the 253 L{SchemaRDD} is not operated on directly, as it's underlying 254. It requires that the schema of the class:DataFrame is the same as the schema of the table. Rather than creating Parquet schema and using ParquetWriter and ParquetReader to write and read file respectively it is more convenient to use a framework like Avro to create schema. This post is about analyzing the Youtube dataset using pyspark dataframes. py, then run it as follows: nmvega@fedora$ ptpython -i. Generally, Spark sql can not insert or update directly using simple sql statement, unless you use Hive Context. We are going to load this data, which is in a CSV format, into a DataFrame and then we. # Parquet files are self-describing so the schema is preserved. Parquet file in Spark Basically, it is the columnar information illustration. wholeTextFiles => file, 내용리턴) md = sc. My case is to perform multiple joins and groups, sorts and other DML, DDL operations on it to get to the final output. Topic: This post describes a data pipeline for a machine learning task of interest in high energy physics: building a particle classifier to improve event selection at the particle detectors. If you're going to specify a custom schema you must make sure that schema matches the data you are reading. I am new to Pyspark and nothing seems to be working out. sql('select * from tiny_table') df_large = sqlContext. When the input format is supported by the DataFrame API e. # DataFrames can be saved as Parquet files, maintaining the schema information. 3, but we've recently upgraded to CDH 5. Getting The Best Performance With PySpark 1. For example, you can read and write Parquet files using Pig and MapReduce jobs. Even though you can print the schema and run show() ok, you cannot apply any. This tool parses xml files automatically (independently of their structure), and explodes their arrays if needed, and inserts them in a new HiveQL table, to make this data accesible for data analysis. Dataframes in Pyspark can be created in multiple ways: Data can be loaded in through a CSV, JSON, XML or a Parquet file. A SparkSession can be used to create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. textFile(args[0]). from pyspark. Power at scale High performance on petabyte-scale data volumes With its unique cost-based query optimizer designed for large-scale data workloads, Greenplum scales interactive and batch-mode analytics to large datasets in the petabytes without degrading query performance and throughput. Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie Strickland 1. Of course, Spark SQL also supports reading existing Hive tables that are already stored as Parquet. And fortunately parquet provides support for popular data serialization libraries, like avro, protocol buffers and thrift. [2/4] spark git commit: [SPARK-5469] restructure pyspark. PySpark recipes¶ DSS lets you write recipes using Spark in Python, using the PySpark API. With the emergence of new technologies that make data processing lightening fast, and cloud ecosystems which allow for flexibility, cost savings, security, and convenience, there appear to be some…. “header” set to true signifies the first row has column names. 1> RDD Creation a) From existing collection using parallelize meth. Parquet files not only preserve the schema information of the dataframe, but will also compress the data when it gets written into HDFS. The example reads the users. To work with Hive, we have to instantiate SparkSession with Hive support, including connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions if we are using Spark 2. Power at scale High performance on petabyte-scale data volumes With its unique cost-based query optimizer designed for large-scale data workloads, Greenplum scales interactive and batch-mode analytics to large datasets in the petabytes without degrading query performance and throughput. "inferSchema" instructs Spark to attempt to infer the schema of the CSV and finally load function passes in the path and name of the CSV source file. Working on Parquet files in Spark. Pyspark ( Apache Spark with Python ) – Importance of Python. Typically these files are stored on HDFS. You can vote up the examples you like or vote down the exmaples you don't like. 0, you can easily read data from Hive data warehouse and also write/append new data to Hive tables. But let's take a step back and discuss what schema evolution means. Working on Parquet files in Spark. parquet(tempdir) print (" Schema from. In this tutorial I will demonstrate how to process your Event Hubs Capture (Avro files) located in your Azure Data Lake Store using Azure Databricks (Spark). sql('select * from massive_table') df3 = df_large. insertInto , which inserts the content of the DataFrame to the specified table, requires that the schema of the class:DataFrame is the same as the schema of the table. The schema of the rows selected are the same as the schema of the table Since the function pyspark. Loading Get YouTube without the ads. The advantages of having a columnar storage are as follows − Spark SQL provides support for both reading and writing parquet files that automatically capture the schema of the original data. Parquet tables created by Impala can be accessed by Hive, and vice versa. Generate Schema. This work is fully open source (Apache-2. Conceptually, it is equivalent to relational tables with good optimization techniques. The second option to create a dataframe is to read it in as RDD and change it to dataframe by using the toDF dataframe function or createDataFrame from SparkSession. 0, you can easily read data from Hive data warehouse and also write/append new data to Hive tables. from pyspark. DataFrameto HDFS and read it back later on, to save data between sessions, or to cache the result of some preprocessing. File source - Reads files written in a directory as a stream of data. Menu Benchmarking Impala on Kudu vs Parquet 05 January 2018 on Big Data, Kudu, Impala, Hadoop, Apache Why Apache Kudu. PySpark Cheat Sheet: Spark in Python Apache Spark is generally known as a fast, general and open-source engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. parquet function that returns an RDD of JSON strings using the column names and schema to. rxin Mon, 09 Feb 2015 20:58:51 -0800. 251 252 For normal L{pyspark. As per the SPARK API latest documentation def text(path: String): Unit Saves the content of the [code ]DataFrame[/code] in a text file at the specified path. A SQLContext can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. Use Spark SQL for ETL. In the middle of the code, we are following Spark requirements to bind DataFrame to a temporary view. DataType` or a datatype string or a list of. That is, it consists of rows and columns of data that can, for example, store the results of an SQL-style query. Avro is a row-oriented remote procedure call and data serialization framework developed within Apache's Hadoop project. Article Introduction. The Apache Parquet project provides a standardized open-source columnar storage format for use in data analysis systems. AWS Glue generates the schema for your semi-structured data, creates ETL code to transform, flatten, and enrich your data, and loads your data warehouse on a recurring basis. Background Apache Spark is a general-purpose cluster computing engine with APIs in Scala, Java and Python and libraries for streaming, graph processing and machine learning. pyspark: TypeError: IntegerType can not accept object in type ; apache spark - Can not infer schema for type: when converted RDD to DataFrame; apache spark sql - Read parquet file having mixed data type in a column. Compared to any traditional approach where the data is stored in a row-oriented format, Parquet is more efficient in the terms of performance and storage. There will not be just one dailydata. schema(schema). 2 使用自动类型推断的方式创建dataframe 2. File source - Reads files written in a directory as a stream of data. 3, SchemaRDD will be renamed to DataFrame. A Databricks table is a collection of structured data. Parquet is an open source file format for Hadoop/Spark and other Big data frameworks. SparkSession (sparkContext, jsparkSession=None) [source] ¶. When Avro data is read, the schema used when writing it is always present. By default, we select smaller physical types in our output Parquet file for certain columns because they only contain small values that fit in smaller types than what the schema would suggest. # The result of loading a parquet file is also a DataFrame. Spark SQL - 10 Things You Need to Know 1. As it turns out, real-time data streaming is one of Spark's greatest strengths. Apache Spark is written in Scala programming language. Parquet files are self-describing so the schema is preserved. Something we've only begun to touch on so far is the benefit of utilizing Apache Spark is larger-scale data pipelines. This would not happen in reading and writing XML data but writing a DataFrame read from other sources. Spark SQL module also enables you to access a variety of data sources, including Hive, Avro, Parquet, ORC, JSON, and JDBC. 这里介绍Parquet,下一节会介绍JDBC数据库连接。 Parquet是一种流行的列式存储格式,可以高效地存储具有嵌套字段的记录。Parquet是语言无关的,而且不与任何一种数据处理框架绑定在一起,适配多种语言和组件,能够与Parquet配合的组件有:. Pandas is a good example of using both projects. プロパティ名 デフォルト 意味; spark. 5 and Spark 1. In this part, you will learn various aspects of PySpark SQL that are possibly asked in interviews. In this post I will try to explain what happens when Apache Spark tries to read a parquet file. Similar to the read interface for creating static DataFrame, you can specify the details of the source – data format, schema, options, etc. They are extracted from open source Python projects. Python is a general purpose, dynamic programming language. In my first real world machine learning problem, I introduced you to basic concepts of Apache Spark like how does it work, different cluster modes in Spark and What are the different data representation in Apache Spark. Sadly, the process of loading files may be long, as Spark needs to infer schema of underlying records by reading them. Port details: spark Fast big data processing engine 2. Working on Parquet files in Spark. engine, interfaces Python commands with a Java/Scala execution core, and thereby gives Python programmers access to the Parquet format. parquet(" people. I save a Dataframe using partitionBy ("column x") as a parquet format to some path on each worker. Spark SQL can also be used to read data from an existing Hive installation. JavaBeans and Scala case classes representing. To support Python with Spark, Apache Spark community released a tool, PySpark. This is a guest post by Rafi Ton, founder and CEO of NUVIAD. This is because schema changes can occur in real time. SparkML里的核心API已经换成了DataFrame,为了使读取到的值成为DataFrame类型,我们可以直接使用读取CSV的方式来读取文本文件,可问题来了,当文本文件中每一行的各个数据被不定数目. An instance of Unischema is serialized as a custom field into a Parquet store metadata, hence a path to a dataset is sufficient for reading it. Even though you can print the schema and run show() ok, you cannot apply any. parquetDF = spark. They are extracted from open source Python projects. Prepare your clickstream or process log data for analytics by cleaning, normalizing, and enriching your data sets using AWS Glue. UnischemaField [source] ¶ A type used to describe a single field in the schema: name: name of the field. Supported file formats are text, csv, json, orc, parquet. One cool feature of parquet is that is supports schema evolution. Apache Spark 2. One of the notable improvements is ORC suppor…. count() <-- action. That is, it consists of rows and columns of data that can, for example, store the results of an SQL-style query. Parquet file in Spark Basically, it is the columnar information illustration. Parquet files not only preserve the schema information of the dataframe, but will also compress the data when it gets written into HDFS. 5, with more than 100 built-in functions introduced in Spark 1. csv having below data and I want to find a list of customers whose salary is greater than 3000. Note: If you keep the schema flat (without nesting), the Parquet files you create can be read by systems like Shark and Impala. The Parquet format stores column groups contiguously on disk; breaking the file into multiple row groups will cause a single column to store data discontiguously. I am now using the same file name, not a different one. The other way: Parquet to CSV. Spark SQL is a Spark module for structured data processing. Reading an empty folder as parquet used to return an empty dataframe up till 2. First, the files may not be readable (for instance, they could be missing, inaccessible or corrupted). When the input format is supported by the DataFrame API e. The interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. schema(schema). If you want to read data from a DataBase, such as Redshift, it's a best practice to first unload the data to S3 before processing it with Spark. This is a guest post by Rafi Ton, founder and CEO of NUVIAD. Files will be in binary format so you will not able to read them. We are using Spark-sql and Parquet data-format. This can only be used to assign a new storage level if the RDD does not have a storage level set yet. This will override ``spark. A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. Old ORC files may be incorrect information inside TIMESTAMP. but you can use a library to read it) converting to Parquet is just a matter of reading the input format on one side and. But it will trigger schema inference, spark will go over RDD to determine schema that fits the data. For a 8 MB csv, when compressed, it generated a 636kb parquet file. Schema inference and explicit definition. the StructType pieces in the pyspark. exploded_fields = [s for s in result. Spark SQL 10 Things You Need to Know 2. class pyspark. def json (self, path, schema = None): """ Loads a JSON file (one object per line) or an RDD of Strings storing JSON objects (one object per record) and returns the result as a :class`DataFrame`. csv having below data and I want to find a list of customers whose salary is greater than 3000. parquetFile = spark. parquetDF = spark. A SQLContext can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. from pyspark. 03/11/2019; 7 minutes to read +6; In this article. Automatic schema conversion Supports most conversions between Spark SQL and Avro records, making Avro a first-class citizen in Spark. createDataframe from Row with Schema. py # Use python(1) if you don’t use ptpython. Remember, we have to use the Row function from pyspark. It allows to transform RDDs using SQL (Structured Query Language). The parquet schema is automatically derived from HelloWorldSchema. Spark supports text files (compressed), SequenceFiles, and any other Hadoop InputFormat as well as Parquet Columnar storage. Parquet is an open source file format for Hadoop/Spark and other Big data frameworks. RDD} operations (map, count, etc. In our next tutorial, we shall learn to Read multiple text files to single RDD. See the CSCAR WEBSITE for information and schedule. The following are code examples for showing how to use pyspark. One way that this can occur is if a long value in python overflows the sql LongType, this results in a null value inside the dataframe. , the schema can be modified according to the changes in the data. org Port Added: 2014-12-20 18:34:31. Parquet schema allows data files "self-explanatory" to the Spark SQL applications through the Data Frame APIs. Hive与Parquet在处理表schema信息的区别: a)Hive不区分大小写,Parquet区分大小写; b)Hive需要考虑列是否为空,Parquet不需要考虑;. # Parquet files are self-describing so the schema is preserved. Dataframe in Spark is another features added starting from version 1. It supports Parquet format via pyarrow for data access. com , our flagship product. In my JSON file all my columns are the string, so while reading into dataframe I am using schema to infer and the reason for that no of. They are extracted from open source Python projects. rdd优点:编译时类型安全编译时就能检查出类型错误面向对象的编程风格直接通过类名点的方式来操作数据缺点:序列化和反序列化的性能开销无论是集群间的通信,还是io操作都需要对对象的结构和数据进行序列化和反. 1_1 devel =1 2. Note: Starting Spark 1. So if we are reading data from csv or other sources, we need to explicitly define the schema in our program. Contribute to apache/spark development by creating an account on GitHub. I set up a spark-cluster with 2 workers. Use Spark SQL for ETL. wholeTextFiles => file, 내용리턴) md = sc. Another benefit is that since all data in a given column is the same datatype (obviously), compression quality is far superior. How does Apache Spark read a parquet file. Reading and Writing Data Sources From and To Amazon S3. It is that the best choice for storing long run massive information for analytics functions. 247 """An RDD of L{Row} objects that has an associated schema. Therefore, Python Spark Lineage generates a filed to field lineage output.
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