Pyspark Join Without Duplicate Columns

Inner Merge / Inner join - The default Pandas behaviour, only keep rows where the merge "on" value exists in both the left and right dataframes. PySpark in Jupyter Notebook. Can anyone please help. In either case, the Pandas columns will be named according to the DataFrame column names. Like this: df_cleaned = df. Lots of examples of ways to use one of the most versatile data structures in the whole Python data analysis stack. Spark's DataFrame API provides an expressive way to specify arbitrary joins, but it would be nice to have some machinery to make the simple case of. Python For Data Science Cheat Sheet PySpark - RDD Basics Learn Python for data science Interactively at www. Joining to subqueries is also useful when you have cardinality problems, that is when you are returning duplicate rows or double-counting in sums, averages, or other aggregates. The UNION, INTERSECT, and EXCEPT clauses are used to combine or exclude like rows from two or more tables. In my continued playing around with the Kaggle house prices dataset, I wanted to find any columns/fields that have null values in them. Get single records when duplicate records exist. The SQL code shown is "Select *" so it will return all the columns. 5 alone; so, we thought it is a good time for revisiting the subject, this time also utilizing the external package spark-csv, provided by Databricks. Changing Rows to Columns Using PIVOT - Dynamic columns for Pivoting in SQL Server In an earlier post I have applied pivoting on one column name ItemColour but here I would like to introduce pivoting on more than one column. Step 1: Create a dataframe with all the required columns from the table. Nonmatching records will have null have values in respective columns. If I want to make nonequi joins, then I need to rename the keys before I join. Nov 20, 2018. window import Window. [SPARK-21144][SQL] Print a warning if the data schema and partition schema have the duplicate columns [SPARK-21150][SQL] Persistent view stored in Hive metastore should be case preserving [SPARK-21159][CORE] Don't try to connect to launcher in standalone cluster mode. 12 and earlier, only alphanumeric and underscore characters are allowed in table and column names. PySpark - SQL Basics Duplicate Values Adding Columns Updating Columns Removing Columns Cheat sheet PySpark SQL Python. I've an pair RDD containing (key, List) but some of the values are duplicate. I tried to find threads with a similar issue without success so I don't really know what information I should provide since the logs aren't very clear, so feel free to ask for more info. SQL Server - Changing Rows to Columns Using PIVOT 2. Pandas is arguably the most important Python package for data science. Number of records in table is around 200000. The issue is DataFrame. We start with a data frame describing probes on a microarray. RANK(): This one generates a new row number for every distinct row, leaving gaps between groups of duplicates within a partition. Let's say I have a spark data frame df1, with several columns (among which the column 'id') and data frame df2 with two columns, 'id' and 'other'. The Oracle INSERT ALL statement is used to add multiple rows with a single INSERT statement. If there is no match, the missing side will contain null. 0 onwards as Hive internally rewrites the load into an INSERT AS SELECT. If the given schema is not pyspark. Another way is by using DDF as the lookup table in a UDF to add the index column to the original DDF using the withColumn method. sqlContext. We could have also used withColumnRenamed() to replace an existing column after the transformation. 3 Apache Arrow is integrated with Spark and it is supposed to efficiently transfer data between JVM and Python processes thus enhancing the performance of the conversion from pandas dataframe to spark dataframe. Number of records in table is around 200000. You need to remove the Select * and use Select col1, col2, col3, col4. In other words, workers do not contribute in the transferring stage (0. The number of columns in each dataframe can be different. Renaming columns in a data frame Problem. My objective is to extract only month and year from that table with a specific name. I tried to find threads with a similar issue without success so I don't really know what information I should provide since the logs aren't very clear, so feel free to ask for more info. You can find all of the current dataframe operations in the source code and the API documentation. Like this: df_cleaned = df. function documentation. SELECT * FROM yr_table PIVOT ( MAX ( MARKS ) FOR (SUBJECT) IN ('MTH' AS MTH, 'PHY' AS PHY, 'CHE' AS CHE, 'BIO' AS BIO) ) ORDER BY 1 You can check below. DataType or a datatype string or a list of column names, default is None. Below is the detailed code which shall help in generating surrogate keys/natural keys/sequence numbers. This method takes three arguments. [/code]The one that has usingColumns (Seq[String]) as second parameter works best, as the columns that you join on won't be duplicate. In a second sheet, perform a Remove Duplicates on the UID column only. However, even though I tripled the number of nodes (from 4 to 12), performance seems not to have changed. These issues could be partially addressed (assuming data fits in the memory) by using only a single SparkContext. The number of columns in each dataframe can be different. The functions are the same except each implements a distinct convention for picking out redundant columns: given a data frame with two identical columns 'first' and 'second', duplicate_columns will return 'first' while transpose_duplicate_columns will return 'second'. without using distinct command. Moreover, we will be handling duplicate records, so make sure you know a thing or two about it. Where there are missing values of the “on” variable in the right dataframe, add empty. Python PySpark script to join 3 dataframes and produce a horizontal bar chart plus summary detail - python_barh_chart_gglot. You can use the IDENTITY property to achieve this goal simply and effectively without affecting load performance. The different arguments to merge() allow you to perform natural join, left join, right join, and full outer join in pandas. Use DataFrame API We know that RDD is a fault-tolerant collection of elements that can be processed in parallel. import org. SQL join two tables related by a composite columns primary key or foreign key Last update on September 19 2019 10:37:27 (UTC/GMT +8 hours) In this page we are discussing such a join, where there is no relationship between two participating tables. Renaming columns in a data frame Problem. Thanks for the reply. The general idea behind the solution is to create a key based on the values of the columns that identify duplicates. The following query will give the same result as the query above, just by using the PIVOT operator. One way is to use a list of column datatypes and the column names and iterate over the same to cast the columns in one loop. udf import UserDefinedFunction, _create_udf. The key is the probe_id and the rest of the information describes the location on t. Another typical example of using the COALESCE function is to substitute value in one column by another when the first one is NULL. Order by more than one column of a table We can display list of students based on their mark and based on their name. Joining External Data Files with Spark DataFrames. The function provides a series of parameters (on, left_on, right_on, left_index, right_index) allowing you to specify the columns or indexes on which to join. Specifies an inner or outer join between two tables. Indexes, including time indexes are ignored. frame" method. They are extracted from open source Python projects. Let's say I have a spark data frame df1, with several columns (among which the column 'id') and data frame df2 with two columns, 'id' and 'other'. i have a query like this SELECT tblClientDocument_Base. What’s left is a Pandas DataFrame with 38 columns. indd Created Date:. Union function in pandas is similar to union all but removes the duplicates which is carried out using concat() and drop_duplicates() function. Get single records when duplicate records exist. Convert Pyspark dataframe column to dict without RDD conversion. which I am not covering here. columns] df. I have a column of date in mm/dd/yyyy format in my table and it's data type is text. In this blog post, we describe our work to improve PySpark APIs to simplify the development of custom algorithms. Now that you have identified all the rows with duplicate content, go through the document and hold the "Control" button down while clicking on the number of each duplicate row as shown below. Renaming columns in a data frame Problem. Thanks for the help. Not a duplicate of [2] since I want the maximum value, not the most frequent item. 13 and later, column names can contain any Unicode character (see HIVE-6013). This can be very expensive relative to the actual data concatenation. The SQL code shown is "Select *" so it will return all the columns. In many "real world" situations, the data that we want to use come in multiple files. If the column name list of the new table contains a column name that is also inherited, the data type must likewise match the inherited column(s), and the column definitions are merged into one. Pyspark Left Join and Filter Example. My objective is to extract only month and year from that table with a specific name. How to transpose / convert columns and rows into single row? How to join multiple rows and columns into a single long row? Maybe, it seems easy for you, because you can copy them one by one and join them into a row manually. Pyspark recipes manipulate datasets using the PySpark / SparkSQL "DataFrame" API. Use below command to perform full join. SQL join two tables related by a composite columns primary key or foreign key Last update on September 19 2019 10:37:27 (UTC/GMT +8 hours) In this page we are discussing such a join, where there is no relationship between two participating tables. It will become clear when we explain it with an example. Changing Rows to Columns Using PIVOT - Dynamic columns for Pivoting in SQL Server In an earlier post I have applied pivoting on one column name ItemColour but here I would like to introduce pivoting on more than one column. all_columns offers a row for each column for every object in a database. I am trying to achieve it using python. However, without a proper table join the query produces a record for every employee, whether or not they are linked to the Zurich shop. This is the easiest and quickest way for combining data from numerous Excel columns into one. 1 (one) first highlighted chunk. Nov 20, 2018. anti_join() return all rows from x where there are not matching values in y, keeping just columns from x. utils import to_str # Note to developers: all of PySpark functions here take string as column names whenever possible. @rocky09 @MarcelBeug. The PIVOT operator takes data in separate rows, aggregates it and converts it into columns. from pyspark. As always, the above is much easier to understand by example. functions import monotonically_increasing_id. Renaming columns in a data frame Problem. In many "real world" situations, the data that we want to use come in multiple files. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Fixed an issue affecting installing Python Wheels in environments without Internet access. Additional load operations are supported by Hive 3. If there is no conflict, then the duplicate columns are merged to form a single column in the new table. If I want to make nonequi joins, then I need to rename the keys before I join. These are generic functions with methods for other R classes. join function: [code]df1. Column methods / treat standard Python scalar as a constant column. If the two dataframes have duplicates based on join values, the match process sorts by the remaining fields and joins based on that row number. /bin/pyspark. Next, load the data files in the project and rename the columns. How to prefix columns names of dataframe efficiently without creating a new dataframe in Pyspark? How to remove 'duplicate' rows from joining the same pyspark. The pandas package provides various methods for combining DataFrames including merge and concat. Additional load operations are supported by Hive 3. SELECT * FROM yr_table PIVOT ( MAX ( MARKS ) FOR (SUBJECT) IN ('MTH' AS MTH, 'PHY' AS PHY, 'CHE' AS CHE, 'BIO' AS BIO) ) ORDER BY 1 You can check below. The goal is to find records that look similar without necessarily being identical field by field. Example usage below. And on the PySpark side, we're gonna keep working on this [inaudible 00:24:06] which captures the faster UDF using Pandas and Arrow. I have 4 columns and ~10K rows. max ("B")). Another typical example of using the COALESCE function is to substitute value in one column by another when the first one is NULL. Consider the following, where we have a DataFrame showing one or more skills associated with a particular group. It has no explicit join clause. Personally I would go with Python UDF and wouldn’t bother with anything else: Vectors are not native SQL types so there will be performance overhead one way or another. I have a column of date in mm/dd/yyyy format in my table and it's data type is text. When there are more than one student got the same mark ( say 88 ) then the names of them will be listed alphabetically. AS SELECT * FROM A UNION SELECT * FROM B; My output table has 333 456 rows. How can I create an AUTO_INCREMENT column in a table that already exists and has data? Allow duplicate. It groups the result-set by two columns - name and lastname. Without any aggregate functions, this query would return the same number of rows as are in the table. Using constraints we can define the rules for valid set of values for a given column. streaming import DataStreamWriter. Is there a more Pyspark way of calculating median for a column of values in a Spark Dataframe?. Combine R Objects by Rows or Columns Description. Tags : apache-spark pyspark-sql Answers 4 So talking of efficiency, since spark 2. 1) Output should be something like:. All data from left as well as from right datasets will appear in result set. The goal is to find records that look similar without necessarily being identical field by field. com | Latest informal quiz & solutions at programming language problems and solutions of. utils import to_str # Note to developers: all of PySpark functions here take string as column names whenever possible. In either case, the Pandas columns will be named according to the DataFrame column names. SQL Server - Changing Rows to Columns Using PIVOT 2. Matrix which is not a type defined in pyspark. I have a column of date in mm/dd/yyyy format in my table and it's data type is text. Left Merge / Left outer join – (aka left merge or left join) Keep every row in the left dataframe. SQL Server - Changing Rows to Columns Using PIVOT 2. Learn to use Union, Intersect, and Except Clauses. badGiop11Ctb="IOP02410210: (DATA_CONVERSION) Character to byte conversion did not. 3 Apache Arrow is integrated with Spark and it is supposed to efficiently transfer data between JVM and Python processes thus enhancing the performance of the conversion from pandas dataframe to spark dataframe. I was working on hyperparameter optimization for neural network. Further, Rows contain the records or data for the columns. Column // Create an example dataframe. How can I create an AUTO_INCREMENT column in a table that already exists and has data? Allow duplicate. - There is no column in the data frame called "row. Python For Data Science Cheat Sheet PySpark - RDD Basics Learn Python for data science Interactively at www. (For the sake of example, I am assuming that you have UID in column A, DATE in column B, and the STATUS in C). Apply summary function to each column. Pandas: Find Rows Where Column/Field Is Null Join For Free. We can count distinct values such as in select count (distinct col1) from mytable;. I do this in a PROC SQL: CREATE TABLE &output_table. The key is not generated from the table data. Doing a left_outer join; Dropping columns in the mapping DataFrame after the join is de-duplicate and upload your data files from the Git. join(df2, usingColumns=Seq("col1", …), joinType="left"). The different arguments to merge() allow you to perform natural join, left join, right join, and full outer join in pandas. reduceByKey(lambda x,y: (x)). There are four basic types of SQL joins: inner, left, right, and full. Use self-join to remove duplicate rows; Use analytics to detect and remove duplicate rows; Delete duplicate table rows that contain NULL values. How to display all rows and columns as well as all characters of each column of a Pandas DataFrame in Spyder Python console. So a drop_duplicates method should be able to either consider a subset of the columns or all of the columns for determining which are "duplicates". Get single records when duplicate records exist. The PIVOT operator takes data in separate rows, aggregates it and converts it into columns. (b,a) and same edges (a,a) or (b,b) got the resulting rdd. other - Right side of the join; on - a string for join column name, a list of column names, , a join expression (Column) or a list of Columns. It will become clear when we explain it with an example. Further, Rows contain the records or data for the columns. RIGHT JOIN (RIGHT OUTER JOIN): This joins restores all columns from the RIGHT table and its coordinated lines from a LEFT table. This should prevent duplicate rows being displayed in your results. [SPARK-26147]Python UDFs in join condition fail even when using columns from only one side of join [SPARK-26211]Fix InSet for binary, and struct and array with null. Let's say I have a spark data frame df1, with several columns (among which the column 'id') and data frame df2 with two columns, 'id' and 'other'. How to add a new column with auto increment ,increment factor, min-value and max value in psql? unique value without auto increment existing int column with. Without any aggregate functions, this query would return the same number of rows as are in the table. In this post we will learn this trick. Basic SQL Join Types. duplicate_columns solves a practical problem. It groups the result-set by two columns - name and lastname. This is a very easy method, and I use it frequently when arranging features into vectors for machine learning tasks. However, even though I tripled the number of nodes (from 4 to 12), performance seems not to have changed. This should prevent duplicate rows being displayed in your results. Learn to use Union, Intersect, and Except Clauses. 3 Apache Arrow is integrated with Spark and it is supposed to efficiently transfer data between JVM and Python processes thus enhancing the performance of the conversion from pandas dataframe to spark dataframe. drop_duplicates¶ DataFrame. After figuring out the best hyperparameters, I ran the same model again alone (now no hyperparameter optimization) but I got different results. I tried to find threads with a similar issue without success so I don't really know what information I should provide since the logs aren't very clear, so feel free to ask for more info. It will become clear when we explain it with an example. I would like to discuss to easy ways which isn’t very tedious. Many of the optimizations that I will describe will not affect the JVM languages so much, but without these methods, many Python applications may simply not work. Endnotes In this article, I have introduced you to some of the most common operations on DataFrame in Apache Spark. So let us jump on example and implement it for multiple columns. This is not negotiable. Distinguish columns that have identical names but reside in different tables by using column aliases. Can anyone please help. Vertical partitioning on SQL Server tables may not be the right method in every case. columns = new_column_name_list Can we do the above same step in Pyspark without having to finally create new dataframe? It is inefficient because we will have 2 dataframe with the same data but different column names leading to bad memory utlilization. I am trying to get rid of white spaces from column names - because otherwise the DF cannot be saved as parquet file - and did not find any usefull method for renaming. PySpark - SQL Basics Duplicate Values Adding Columns Updating Columns Removing Columns Cheat sheet PySpark SQL Python. Hot-keys on this page. Using constraints we can define the rules for valid set of values for a given column. You need to remove the Select * and use Select col1, col2, col3, col4. merge operates as an inner join, which can be changed using the how parameter. [code]import pandas as pd fruit = pd. 1, so there may be new functionalities not in this post as the latest version is 2. So Spark is focused on processing (with the ability to pipe data directly from/to external datasets like S3), whereas you might be familiar with a relational database like MySQL, where you have storage and processing built in. This can be handy for bootstrapping or to run quick test analyses on subsets of very large datasets. By voting up you can indicate which examples are most useful and appropriate. How to transpose / convert columns and rows into single row? How to join multiple rows and columns into a single long row? Maybe, it seems easy for you, because you can copy them one by one and join them into a row manually. Left outer join is a very common operation, especially if there are nulls or gaps in a data. As always, the above is much easier to understand by example. How to convert rows to comma separated values along with other columns using FOR XML PATH query in SQL Server. The different arguments to merge() allow you to perform natural join, left join, right join, and full outer join in pandas. Changing Rows to Columns Using PIVOT - Dynamic columns for Pivoting in SQL Server In an earlier post I have applied pivoting on one column name ItemColour but here I would like to introduce pivoting on more than one column. How do I detect the Python version at runtime? [duplicate] How to print objects of class using print()? Getting the class name of an instance? Why does Python code use len() function instead of a length method? Selecting multiple columns in a pandas dataframe; Join a list of items with different types as string in Python. How can I create an AUTO_INCREMENT column in a table that already exists and has data? Allow duplicate. Q&A for Work. This is a very easy method, and I use it frequently when arranging features into vectors for machine learning tasks. The order of the rows passed in as Pandas rows is not guaranteed to be stable relative to the original row order. The SQL CROSS JOIN produces a result set which is the number of rows in the first table multiplied by the number of rows in the second table if no WHERE clause is used along with CROSS JOIN. SQL join two tables related by a composite columns primary key or foreign key Last update on September 19 2019 10:37:27 (UTC/GMT +8 hours) In this page we are discussing such a join, where there is no relationship between two participating tables. In a COMPSs execution without a shared disk, on a conventional file system (case C in the Table 3), the input files are transferred from the master to all requesting workers. sql("SELECT df1. Consider the case where we want to gain insights to aggregated data: dropping entire rows will easily skew aggregate stats by removing records from the total pool and removing records which should have been counted. [SPARK-26181]the hasMinMaxStats method of ColumnStatsMap is not correct. x4_ls = [35. Alias refers to the practice of using a different temporary name to a database table or a column in a table. Is there a best way to add new column to the Spark dataframe? (note that I use Spark 2. Fixed an issue affecting installing Python Wheels in environments without Internet access. Three columns will be generated, and the third (which represents count per group) will be labeled duplicate_count. " Thanks, Larry. I am trying to find out quantiles for each column on the table for various firms using spark 1. Each column has a specific name and data type for the column. AS SELECT * FROM A UNION SELECT * FROM B; My output table has 333 456 rows. Basic SQL Join Types. How to transpose / convert columns and rows into single row? How to join multiple rows and columns into a single long row? Maybe, it seems easy for you, because you can copy them one by one and join them into a row manually. Python- How to make an if statement between x and y? [duplicate]. It groups the result-set by two columns - name and lastname. Left Merge / Left outer join – (aka left merge or left join) Keep every row in the left dataframe. from pyspark. SELECT * FROM student ORDER BY mark , name This will list on ascending order of mark. One way is by inner joining the original DDF with the new DDF on the category columns and dropping the duplicate category column. You can vote up the examples you like or vote down the ones you don't like. The salary information is missing for 111 NBA players, so these will be players we will drop as well when we do an analysis. How to join (merge) data frames (inner, outer, right, left join) in pandas python We can merge two data frames in pandas python by using the merge() function. If the key column in both the left and right array contains duplicates, then the result is a many-to-many merge. How can I create an AUTO_INCREMENT column in a table that already exists and has data? Allow duplicate. I'm a novice My task is to create a script a that examines a list of numbers (for example, 2, 8, 64, 16, 32 4, 16, 8) to determine whether it contains duplicates. We often need to combine these files into a single DataFrame to analyze the data. I often need to perform an inverse selection of columns in a dataframe, or exclude some columns from a query. I have a column of date in mm/dd/yyyy format in my table and it's data type is text. Hi I have a dataframe (loaded CSV) where the inferredSchema filled the column names from the file. The second argument, on, is the name of the key column(s) as a string. Summarising the DataFrame. The Oracle INSERT ALL statement is used to add multiple rows with a single INSERT statement. This allows you to filter your results to just user tables if you so desire without having to join to the sys. x column name matches one of y, and if no. from pyspark. I tried to find threads with a similar issue without success so I don't really know what information I should provide since the logs aren't very clear, so feel free to ask for more info. Otherwise, it returns the value of the state column. After figuring out the best hyperparameters, I ran the same model again alone (now no hyperparameter optimization) but I got different results. other - Right side of the join; on - a string for join column name, a list of column names, , a join expression (Column) or a list of Columns. Developing custom Machine Learning (ML) algorithms in PySpark—the Python API for Apache Spark—can be challenging and laborious. We want to support the Pandas UDF function with more PySpark functions, for instance groupBy aggregation and window functions. I am trying to get rid of white spaces from column names - because otherwise the DF cannot be saved as parquet file - and did not find any usefull method for renaming. We can use. The complexity of the algorithm used is proportional to the length of the answer. The SQL GROUP BY statement is used together with the SQL aggregate functions to group the retrieved data by one or more columns. (b,a) and same edges (a,a) or (b,b) got the resulting rdd. max ("B")). PL/SQL - how can we avoid duplicate rows. Spark automatically removes duplicated “DepartmentID” column, so column names are unique and one does not need to use table prefix to address them. Pyspark Dataframe Row To Json. Operation filter is take predicate f(x) as an argument which is some thing like x % 2 == 0 it means it will return true for even elements and false for odd elements. The key is not generated from the table data. Learn how to slice and dice, select and perform commonly used operations on DataFrames. If you have any idea what JOINS are and you are familiar with the INNER JOIN type, learning how to use the LEFT JOIN in SQL should be a walk in the park. SQL Server - Changing Rows to Columns Using PIVOT 2. The join condition is very specific: we chose to match records that have at least 2 fields that are equal. e, if we want to remove duplicates purely based on a subset of columns and retain all columns in the original dataframe. The relationship between the two tables above is the "CustomerID" column. Once the data has been loaded into Python, Pandas makes the calculation of different statistics very simple. Lets see how to use Union and Union all in Pandas dataframe python. In a previous post, we glimpsed briefly at creating and manipulating Spark dataframes from CSV files. In A there are 1 121 776 rows. Python PySpark script to join 3 dataframes and produce a horizontal bar chart plus summary detail - python_barh_chart_gglot. This should prevent duplicate rows being displayed in your results. A word of caution: it's important to be VERY careful so as not to duplicate columns when using a SQL join. sqlContext. After figuring out the best hyperparameters, I ran the same model again alone (now no hyperparameter optimization) but I got different results. 0 (zero) top of page. The names of the key column(s) must be the same in each table. Three columns will be generated, and the third (which represents count per group) will be labeled duplicate_count. Personally I would go with Python UDF and wouldn't bother with anything else: Vectors are not native SQL types so there will be performance overhead one way or another. Using constraints we can define the rules for valid set of values for a given column. (b,a) and same edges (a,a) or (b,b) got the resulting rdd. Pyspark Left Join and Filter Example. A semi join does not eliminate existing duplicates. Want to join two R data frames on a common key? Here's one way do a SQL database style join operation in R. to ensure that there are still a sufficient number of records left to train a predictive model. I do this in a PROC SQL: CREATE TABLE &output_table. We start with a data frame describing probes on a microarray. other FROM df1 JOIN df2 ON df1. Pyspark Left Join and Filter Example. readwriter import DataFrameWriter from pyspark. And on the PySpark side, we're gonna keep working on this [inaudible 00:24:06] which captures the faster UDF using Pandas and Arrow. As always, the above is much easier to understand by example. The current default of sorting is deprecated and will change to not-sorting in a future version of pandas. From your question, it is unclear as-to which columns you want to use to determine duplicates. dplyr::mutate(iris, sepal = Sepal. All columns show values relative to the size of the file (20 GB, in this case). Now, you have a key-value RDD that is keyed by columns 1,3 and 4. Is there a better method to join two dataframes and not have a duplicated column? pyspark dataframes join column. Otherwise, it returns the value of the state column. Agree with David. We got the rows data into columns and columns data into rows. groupBy ("A"). Inner Merge / Inner join - The default Pandas behaviour, only keep rows where the merge "on" value exists in both the left and right dataframes. In B there are 114 028 rows.