Spark dataframe write partitionby. parquet ("Partition file path") -- it worked .

Spark dataframe write partitionby e. repartition () method is used to increase or decrease the RDD/DataFrame partitions by number of partitions or by single column name or multiple column names. Let's start by writting a partitioned dataframe like this: df. Likewise, if you join two dataframe by a column, Spark will automatically repartition both dataframes by that column. 5. sources. write to access this. partitionBy() is a DataFrameWriter method that specifies if the data should be written to disk in folders. The dataframe can be stored to a Hive table in parquet format using the method df. DataFrameWriter(df: DataFrame) ¶ Interface used to write a DataFrame to external storage systems (e. read. write . I need to separate the events, dependin Oct 12, 2018 · I read this data using Apache spark and I want to write them partition by id column. mode ("overwrite"). IncrementalTransformOutput. repartition(COL). I want to read a directory containing json files. Is there any way to partition the dataframe by the column city and write the parquet files? Aug 15, 2023 · What is PartitionBy in Apache Spark? PartitionBy is a feature in Spark designed to distribute the data into separate folders based on a specific column. var1="country","state" (Getting the partiton column names of hive table) pyspark. saveAsTable("tbl") produces 100 files of roughly Nov 15, 2021 · Here, you are asking spark to write the existing dataframe into output_path partitioned by the distinct values of the column "partition". partitionOverwriteMode setting to dynamic, the dataset needs to be partitioned, and the write mode overwrite. format("com. repartition(numPartitions, *cols) [source] # Returns a new DataFrame partitioned by the given partitioning expressions. When I use this: df. Jul 4, 2025 · In simple words, repartition () increases or decreases the partitions, whereas coalesce () only decreases the number of partitions efficiently. partitionBy("a"). , over a range of input rows. write(). DataFrame. Let us assume that partitions 1-5 have 5 unique combinations of name and entrance date, partitions 6-10 have Mar 30, 2019 · Returns a new :class: DataFrame that has exactly numPartitions partitions. saveAsTable(name, format=None, mode=None, partitionBy=None, **options) [source] # Saves the content of the DataFrame as the specified table. overwritePartitions() [source] # Overwrite all partition for which the data frame contains at least one row with the contents of the data frame in the output table. Dynamic overwrite example Let's first create a local folder Mar 27, 2024 · 1. Mar 4, 2021 · What is the difference between DataFrame repartition() and DataFrameWriter partitionBy() methods? I hope both are used to "partition data based on dataframe column"? Or is there any difference? Jun 1, 2019 · pyspark. shuffle. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim 10 of the current partitions. Create our custom partitioner and partition our PairRDD with it so we can have one partition for each key. 3 LTS and above supports dynamic partition overwrites for partitioned tables using overwrite mode: either INSERT OVERWRITE in SQL, or a DataFrame write with df. Sep 9, 2021 · I am writing a dataframe to a delta table using the following code: (df . Window. option("partitionOverwriteMode", "dynamic"). hdfs://d Nov 9, 2023 · Helpful for joins. Write a Single file using Spark coalesce () & repartition () When you are ready to write a DataFrame, first use Spark repartition () and coalesce () to merge data from all partitions into a single partition and then save it to a file. option ("header",True). Aug 9, 2018 · I've been trying to partition and write a spark dataframe to S3 and I get an error. builder. Spark Partitioning Advantages Jul 17, 2023 · The repartition() function in PySpark is used to increase or decrease the number of partitions in a DataFrame. Example: df. mode ("overwrite") . The number of created folders corresponds Oct 9, 2024 · Simplifying Dynamic Partition Overwrite in Spark: A Guide to PartitionOverwriteMode When you’re dealing with large amounts of data in Apache Spark, managing your data efficiently becomes important … Nov 8, 2023 · This tutorial explains how to use the partitionBy() function with multiple columns in a PySpark DataFrame, including an example. in dfAvro. We’ll cover all relevant methods, parameters, and optimization techniques, ensuring a clear understanding of how they drive PySpark 使用 partitionBy 进行分区数据的处理 在本文中，我们将介绍如何使用 PySpark 的 partitionBy 方法来对数据进行分区处理。 分区是将数据划分成不同的部分，以提高查询和分析的效率。 通过对数据进行分区，可以使得数据的存储和访问更加高效。 This is expected and desired behavior. text ("/path/to/output") The text files will be encoded as UTF-8. Each row becomes a new line in the output file. Jan 14, 2016 · I would like to repartition / coalesce my data so that it is saved into one Parquet file per partition. In this example, we create a Spark DataFrame containing sales data with a date column. csv file. Meaning, it first partitions by the key and then repartitions to the number. partitionBy("Season"). partitionBy("source"). functions as func # Create a spark session using getOrCreate() function spark_session = SparkSession. DataFrameWriterV2 # class pyspark. Usage Apr 2, 2025 · What is partitionBy in Spark? The partitionBy method in Spark is used to control how data is stored physically on disk when writing to storage systems like ADLS, Amazon S3, etc. mode("overwrite"). SET spark. Jul 23, 2025 · In this article, we are going to learn data partitioning using PySpark in Python. PySpark partitionBy () is a method of DataFrameWriter class which is used to write the DataFrame to disk in partitions, one sub-directory for each unique value in the partition columns. Understanding Apache Spark Partitioning: A Comprehensive Guide We’ll define partitioning, detail how it works with RDDs and DataFrames, and provide a practical example—a sales data analysis—to illustrate its impact on performance. save ("Partition file path") -- It doesnt seems to work for me. Broadcast Join – Broadcasts small DataFrame to all nodes. I'm wanting to define a custom partitioner on DataFrames, in Scala, but not seeing how to do this. This is similar to Hive's partitioning scheme and is done for optimization purposes. 0: SPARK-20236 To use it, you need to set the spark. mode(SaveMode. The ab Mar 14, 2024 · When writing a Spark DataFrame to files like Parquet or ORC, ‘the partition count and the size of each partition’ is one of the main concerns. partitionBy works as follows: For every partition of the dataframe, get the unique values of the columns in partitionBy argument Write the data for every unique combination in a different file In your example above, let us say your dataframe has 10 partitions. Feb 28, 2023 · When you write Spark Data frame to disk by calling partition By (), Pyspark splits the records based on the partition column and stores each partition data into a sub-directory. parquet(s"$path") It collects the events with a common schema, converts to a DataFrame, and then writes out as parquet. any pointers would be helpful I'm fairly new to apache spark. Examples -------- Write a DataFrame into a Parquet file in a buckted manner, and read it back. createDataFrame ( [ (100, "Hyukjin Kwon"), (120, "Hyukjin Sep 3, 2025 · Databricks Runtime 11. partitionBy(*cols: Union[str, List[str]]) → pyspark. repartition ¶ DataFrame. May 19, 2021 · bucketBy is intended for the write once, read many times scenario, where the up-front cost of creating a persistent bucketised version of a data source pays off by avoiding a costly shuffle on read in later jobs. AnalysisException: Partition column data. spark. In this article, I’ve explained the concept of window functions, syntax, and finally how to use them with PySpark SQL and PySpark DataFrame API. Whether you’re archiving data, enabling efficient querying, or integrating with other Spark pyspark. write ¶ property DataFrame. Apr 17, 2025 · Read a CSV file and group by Year, for each year write the resulting data in the partition. If you need specific directory structure you should use downstream process to rename directories. DataFrameWriter # class pyspark. Syntax DataFrameWriter. format ("delta"). Generic Load/Save Functions Manually Specifying Options Run SQL on files directly Save Modes Saving to Persistent Tables Bucketing, Sorting and Partitioning In the simplest form, the default data source (parquet unless otherwise configured by spark. getOrCreate() # Read the CSV file data_frame Oct 26, 2021 · The partitionBy () method, which is used to write a DataFrame to disk in partitions, creates one sub-folder (partition-folder) for each unique value of the specified column. May 23, 2024 · PySpark partitionBy () is used to partition based on column values while writing DataFrame to Disk/File system. Too many partitions with small partition size Jun 28, 2017 · I am trying to leverage spark partitioning. Introduction Spark is a framework which provides parallel and distributed Sep 27, 2018 · I have some data which has timestamp column field which is long and its epoch standard , I need to save that data in split-ted format like yyyy/mm/dd/hh using spark scala data. It is an important tool for achieving optimal S3 storage or effectively Feb 12, 2025 · Versions: Apache Spark 3. These are handy when making aggregate operations in a specific window frame on DataFrame columns. DataFrame foreachPartition () Usage On Spark DataFrame foreachPartition() is similar to foreach() action which is used to manipulate the accumulators, write to a database table or external data sources but the difference being foreachPartiton () gives you an option to do heavy initializations per each partition and is consider most efficient. In the case the table already exists, behavior of this function depends on the save mode, specified by the mode function (default to throwing an exception). repartition(numPartitions: Union[int, ColumnOrName], *cols: ColumnOrName) → DataFrame ¶ Returns a new DataFrame partitioned by the given partitioning expressions. Jun 25, 2025 · PySpark repartition () is a DataFrame method that is used to increase or reduce the partitions in memory and when written to disk, it create all part files in a single directory. Python Scala Java R Feb 1, 2018 · Just to let other people who dont want to patch or write SQL insert statements know, but using a repartition and then a partitionBy on a dataframe actually works as I wanted it to and not how I expected it to. partitionBy (). When you call repartition(), Spark shuffles the data across the network to create new Saves the content of the DataFrame in a text file at the specified path. Oct 25, 2021 · In this post, we’ll learn how to explicitly control partitioning in Spark, deciding exactly where each row should go. partitionBy("month"). id not found in schema I also tried to use explode function like that: Nov 20, 2018 · DataNoon - Making Big Data and Analytics simple!All data processed by spark is stored in partitions. option() and write(). Since new incremental data for a particular day will come in periodically, what I want is to replace only those partitions in the hierarchy that dataFrame has data for, leaving the others coalesce() and repartition() change the memory partitions for a DataFrame. For the first run, a dataframe like this Sep 20, 2021 · Spark is smart enough to do that automatically. saveAsTable # DataFrameWriter. functions import input_file_name >>> # Write a DataFrame into a Parquet file in a bucketed manner. When reading the directory, I see that the directory in the warehouse is partitioned the way I want: Oct 16, 2025 · Pyspark SQL provides methods to read Parquet files into a DataFrame and write a DataFrame to Parquet files, parquet () function from DataFrameReader and DataFrameWriter are used to read from and write/create a Parquet file, respectively. partitionBy() function in Spark is to create a directory structure with partition column names. com DataFrameWriter. Parquet files maintain the schema along with the data, hence it is used to process a structured file. json(<path_to_folder>) I will get error: Exception in thread "main" org. partitionBy(COL) seems like it might work, but I worry that (in the case of a very large table which is about to be partitioned into many folders) having to first combine it to some small number of partitions before doing the partitionBy(COL) seems like a bad idea. The DataFrame must have only one column that is of string type. Spark uses directory structure for partition discovery and pruning and the correct structure, including column names, is necessary for it to work. repartition () is a wider transformation that involves shuffling of the data hence, it is considered an Sep 23, 2019 · The behavior of df. partitionBy method can be used to partition the data set by the given columns on the file system. saveAsTable (name, format=None, mode=None, partitionBy=None, **options) API if you click on the source hyperlink on the right hand side in the documentation you can traverse and find details of the other not so clear arguments eg. write ¶ Interface for saving the content of the non-streaming DataFrame out into external storage. partitionBy("date"). apache. default) will be used for all operations. parquet operation is a key method for saving a DataFrame to disk in Parquet format, a columnar storage file format optimized for big data systems. option ("overwriteSchema","true"). saveAsTable(tablename,mode). Feb 19, 2024 · The default behavior of the . mode ("append")\ . partitionBy("countryFirst"). The "data frame" is defined using the zipcodes. One of the data tables I'm working w pyspark. RDD Partition RDD repartition RDD coalesce DataFrame Partition DataFrame repartition DataFrame coalesce One important point to note is PySpark Jul 18, 2015 · As per the latest spark documentation following are the options that can be passed while writing DataFrame to external storage using . 0+, ensuring efficient data processing and reliability in your projects. parquet("partitioned_parquet/") To read the whole dataframe back in WITH the partitioning variables Dec 5, 2022 · Explore how G-Research addresses in-partition order issues in Apache Spark 3. partitionBy(*cols) [source] # Creates a WindowSpec with the partitioning defined. Whereas partitionBy is useful to meet the data layout requirements of downstream consumers of the output of a Spark job. pyspark. repartition(100). This type of overwrite is only available for classic compute, not Databricks SQL warehouses or serverless compute. Should I repartition Spark Dataframe ? May 29, 2017 · I am new to spark and scala. I would also like to use the Spark SQL partitionBy API. partitionBy ("date") . 4. csv("/tmp/zipcodesState") The Spark Session is defined. Append). We then use the partitionBy method to partition the DataFrame by the date column and save it in the "sales_data" directory. Jul 10, 2015 · I have a sample application working to read from csv files into a dataframe. write_dataframe() with partitionBy=['col1', 'col2', 'col_N'] as described here. And it will know Aug 25, 2022 · PySpark DataFrameWriter. overwritePartitions # DataFrameWriterV2. DataFrameWriterV2. conf. partitionBy ("year","month"). Through methods like repartition (), coalesce (), and partitionBy () on a DataFrame, tied to SparkSession, you can control how data is Dec 11, 2019 · I have tab delimited data(csv file) like below: 201911240130 a 201911250132 b 201911250143 c 201911250223 z 201911250224 d I want to write directory group by year, month, day, hour. partitionBy(*cols: Union[str, List pyspark. csv"). May 11, 2020 · Let’s go through the steps: Convert the dataframe to a PairRDD [ (K, V)] with the value being the column we want to sort, and the key being the columns we partition by. sql import SparkSession from pyspark. text ("/path/to/output") // Java: df. partitionOverwriteMode", "DYNAMIC") Let's use some examples to understand more. In this article, we shall discuss the different write options Spark supports along with a few examples. DataFrame to external storage using the v2 API. It provides high-level APIs in Java, Scala, Python, and R and an optimized engine that supports general execution graphs. Similar to coalesce defined on an :class: RDD, this operation results in a narrow dependency, e. 0. This tutorial covers the basics of Delta tables, including how to create a Delta table, write data to a Delta table, and read data from a Delta table. As you can see, a deep understanding of partitioning is required to optimize PySpark workloads. Hive table is partitioned on mutliple column. Understanding Apache Spark Partitioning and Shuffling: A Comprehensive Guide We’ll define partitioning and shuffling, detail their interplay in RDDs and DataFrames, and provide a practical example—a sales data analysis—to illustrate their impact on performance. parquet ('s3a://bucket PartitionBy Operation in PySpark: A Comprehensive Guide PySpark, the Python interface to Apache Spark, is a powerful framework for distributed data processing, and the partitionBy operation on Resilient Distributed Datasets (RDDs) offers a targeted way to partition Pair RDDs using a custom partitioning strategy. So, when it generates the physical plan, it will directly skip reading the files in partitions that are going to get filtered anyways. In PySpark, data partitioning refers to the process of dividing a large dataset into smaller chunks or partitions, which can be processed concurrently. You can also create UDF to df. DataFrameWriter. When you want to restrict number of output file parts generated during spark dataframe write. Apache Spark is a unified analytics engine for large-scale data processing. 3. sql. Now my requirement is to include OP_CARRIER field also in 2nd dataframe i. When mode is Overwrite, the schema of pyspark. repartition # DataFrame. parquet ("Partition file path") -- it worked Nov 16, 2023 · Understanding spark's partition discoverySuppose you read the partitioned data into a dataframe, and then filter the dataframe on one of the partition columns. Jun 22, 2022 · How to enable DYNAMIC overwrite Dynamic overwrite mode can be enabled when saving DataFrame: dataframe. You As mentioned in this question, partitionBy will delete the full existing hierarchy of partitions at path and replaced them with the partitions in dataFrame. class pyspark. True, but is there an alternative to it that doesn't require using this position-based function? Feb 7, 2023 · 1. 1 After publishing a release of my blog post about the insertInto trap, I got an intriguing question in the comments. It is a convenient way to persist the data in a structured format for further processing or analysis. . partitionBy # static Window. I want to write the dataframe data into hive table. Repartitioning is one powerful tool in your toolkit. saveAsTable operation is a key method for saving a DataFrame as a persistent table in a metastore, such as Hive, making it accessible for querying across Spark sessions. See full list on sparkbyexamples. json("foo"); df. PySpark Partition is a way to split a large dataset into smaller datasets based on one or more partition keys. You are nowhere asking spark to reduce the existing partition count of the dataframe. Parameters numPartitionsint can be an int to specify the target number of partitions or a Column. It's included here to show the difference in behavior -- of a query when `CLUSTER BY` is not used vs when it's used. Jan 11, 2024 · Spark Partitioning vs Bucketing partitionBy vs bucketBy As a data analyst or engineer, you may often come across the terms “partitioning” and “bucketing” in your work with large datasets. options() methods provide a way to set options while writing DataFrame or Dataset to a data source. The file has attribute called "EVENT_NAME" which can have 20 different values. spark_write_parquet Description Serialize a Spark DataFrame to the Parquet format. format ("delta") . file systems, key-value stores, etc). partitions = 2; -- Select the rows with no ordering. write (). Ideally, it would look like this: df. Jul 26, 2022 · . dataframe. DataFrameWriterV2(df, table) [source] # Interface used to write a class: pyspark. Write our . save(path) Alternatively, we can also use Spark SQL configuration: spark. This function takes 2 parameters; numPartitions and *cols, when one is specified the other is optional. id"). partitionBy("data. Nov 24, 2020 · Iteration using for loop, filtering dataframe by each column value and then writing parquet is very slow. df. If it is a Column, it will be used as Apr 7, 2022 · Hi All, I am trying to Partition By () on Delta file in pyspark language and using command: df. Oct 24, 2016 · Then from my code I do something like this: DataFrame df = sqlContext. partitionBy ("key"). The query below produces rows -- where age column is not Also you can improve the performance of dataframe transformations like joins , merge by repartitioning it on the basis of some key columns. For example: // Scala: df. DataFrameWriter(df) [source] # Interface used to write a DataFrame to external storage systems (e. Jul 23, 2025 · # Python program to partition by multiple # columns in PySpark with columns in a list # Import the SparkSession, Window and functions libraries from pyspark. When you write DataFrame to Disk by calling partitionBy () Pyspark splits the records based on the partition column and stores each partition data into a sub-directory. For example, when a table is partitioned by day, it may be stored in a directory layout like: table Jun 9, 2018 · Something like df. json("output"); Everything is fine and I get the following structure at the output: output | |-->a=2 |-> part-blahblah When I inspect the contents of part-blahblah then the "a" field is missing from the Jul 7, 2017 · //save the data with partitionby first letter of country result. Use DataFrame. This operation is equivalent to Hive’s INSERT OVERWRITE … PARTITION, which replaces partitions dynamically depending on the contents of the data frame. Further, when the PySpark DataFrame is written to disk by calling the partitionBy () then PySpark splits the records based on the partition column and stores each of the partition data into the sub-directory so, it creates 6 Apr 19, 2022 · I have seen methods for inserting into Hive table, such as insertInto(table_name, overwrite =True, but I couldn't work out how to handle the scenario below. Memory partitioning is often important independent of disk partitioning. >>> from pyspark. The resulting DataFrame is hash partitioned. When specified, the table data will be stored by these values for efficient reads. format and options which are Jun 13, 2022 · To be able to use Spark Partition Pruning in Palantir Foundry we need to use transforms. Other common use-case for repartition is during dataframe write operation. Key Takeaways Mar 27, 2024 · The Spark write(). write. DataFrameWriter ¶ Partitions the output by the given columns on the file system. Any suggestions are greatly appreciated! Jun 23, 2015 · I've started using Spark SQL and DataFrames in Spark 1. read(). It’s a transformation operation, meaning it’s lazy; Spark plans the repartitioning but waits for an action like show to execute it. partitionedBy # DataFrameWriterV2. parquet Operation in PySpark DataFrames: A Comprehensive Guide PySpark’s DataFrame API is a powerful tool for big data processing, and the write. Through, Hivemetastore client I am getting the partition column and passing that as a variable in partitionby clause in write method of dataframe. What is Auto-Write? Auto-write refers to the automatic process of managing how data is written to storage in distributed environments like Spark, Delta Lake, or Databricks. You also have to remember that partitioning drops the columns used for partitioning. The data layout in the file system will be similar to Hive's partitioning tables. Spark partition pruning can benefit from this data layout in file system to improve performance when filtering on partition columns. Dec 23, 2022 · This recipe helps you to perform PartitionBy in spark scala while writing DataFrame to a DBFS path. Finally! This is now a feature in Spark 2. PartitionBy – Partitions when writing data like with df. option("infersche DataFrameWriter is the interface to describe how data (as the result of executing a structured query) should be saved to an external data source. Aug 22, 2020 · It reads data successfully, but as expected OP_CARRIER column is not available in dfAvro dataframe as it is a partition column of the first job. Nov 3, 2020 · Let's say I have a pyspark dataframe that I am working with and currently I can repartition the dataframe as such: dataframe. Parameters colsstr or list name of columns Examples Write a DataFrame into a Parquet file in a partitioned manner, and read it back. format("csv"). I was trying to do something like data. partitionedBy(col, *cols) [source] # Partition the output table created by create, createOrReplace, or replace using the given columns or transforms. Example: May 3, 2016 · I'm trying to write a dataframe in spark to an HDFS location and I expect that if I'm adding the partitionBy notation Spark will create partition (similar to writing in Parquet format) folder in f Partitioning Strategies in PySpark: A Comprehensive Guide Partitioning strategies in PySpark are pivotal for optimizing the performance of DataFrames and RDDs, enabling efficient data distribution and parallel processing across Spark’s distributed engine. readwriter. By default, Spark does not write data to disk in nested folders. saveAsTable($"{CURRENT_SOURCE_VALUE}") Is it possible to accomplish this using partitionBy or should try doing something else like looping over each row using rdd , or possibly groupBy? etc. This still creates a directory and write a single part file inside a directory instead of multiple part files. Let’s Create a DataFrame by Nov 7, 2022 · The partitionBy () method, which is used to write a DataFrame to disk in partitions, creates one sub-folder (partition-folder) for each unique value of the specified column. databricks. window import Window import pyspark. So I could do that like this: df. saveAsTable ("table&q Learn how to write a dataframe to a Delta table in PySpark with this step-by-step guide. We’ll cover all relevant methods, parameters, and optimization techniques, ensuring a clear understanding of how partitioning drives Spark’s parallelism Nov 5, 2025 · When you write Spark DataFrame to disk by calling partitionBy(), PySpark splits the records based on the partition column and stores each partition data into a sub-directory. sql ("DROP TABLE IF EXISTS bucketed_table") >>> spark. set("spark. Returns DataFrameWriter Sep 23, 2025 · PySpark Window functions are used to calculate results, such as the rank, row number, etc. g. Mar 22, 2016 · You need to be careful how you read in the partitioned dataframe if you want to keep the partitioned variables (the details matter). Today we discuss what are partitions, how partitioning works in Spark (Pyspark), why it matters and how the user can manually control the partitions using repartition and coalesce for effective distributed computing. save("outputpath") Edited: You can also use the substring which can increase the performance as suggested by Raphel as pyspark. partitionBy ("Partition Column"). After executing this code, the "sales_data" directory will contain subdirectories for each unique date value in the "date" column, making it easy to locate and query data based on Apr 30, 2022 · Learn about data partitioning in Apache Spark, its importance, and how it works to optimize data processing and performance. Jan 8, 2024 · Spark Partitioning Partition Understanding Do you find yourself struggling with managing large datasets in your Spark projects? Are you looking to optimize your data processing pipelines for … Sep 25, 2024 · 4. This is an important aspect of distributed computing, as it allows large datasets to be processed more efficiently by dividing the workload among multiple Sep 10, 2024 · pyspark. sortByAlphabet () and . _ = spark. Now, the spark planner will recognize that some partitions are being filtered out. In this article, you will learn the difference between PySpark repartition vs coalesce with examples. The alternative to the insertInto, the saveAsTable method, doesn't work well on partitioned data in overwrite mode while the insertInto does. Write. partitionBy(" Jul 28, 2018 · 5 I am a newbie in Spark. parquet ("/location") The issue here each partition creates huge number of parquet files The repartition method in PySpark DataFrames redistributes the data of a DataFrame across a specified number of partitions or according to specific columns, returning a new DataFrame with the reorganized data. api. Please note that without any sort directive, the results -- of the query is not deterministic. repartition(200, col_name) And I write that partitioned dataframe out to a parquet file. saveAsTable Operation in PySpark DataFrames: A Comprehensive Guide PySpark’s DataFrame API is a powerful tool for big data processing, and the write. sortByFrequency () functions. huj lyin amtsix zadqvgqos zfhaly ljgga hup qvfsebc ftceyt mwttgvr jlvkra xussmr tuzj imicr pnhxj