Spark Read Json From S3 Python

Information formatted as JSON can be stored as text in standard SQL Server columns and SQL Server provides functions that can retrieve values from these JSON objects. Read the data from the hive table. Python Read JSON from HTTP Request of URL. The latter option is also useful for reading JSON messages with Spark Streaming. The file is 758Mb in size and it takes a long time to do something very. Rapidly create and deploy powerful Java applications that integrate with JSON web services. I currently have mounted a JSON file from an S3 bucket and I am trying to read in the JSON data but I am unsure of how to do so. It is available so that developers that use older versions of Python can use the latest features available in the json lib. Pip Install. Installation pip install databricks-utils Features. Initially we'll construct Python dictionary like this: # Four Fundamental Forces with JSON d = {} d ["gravity"] = { "mediator":"gravitons", "relative. Even though Spark itself is. A JupyterLab extension that supports Vega 3. Spark SQL supports many built-in transformation functions in the module pyspark. I am trying to read a JSON file, from Amazon s3, to create a spark context and use it to process the data. If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults. int:n n bits as a signed integer. Related posts and tools¶. The Avro data source supports reading and writing Avro data from Spark SQL: Automatic schema conversion Supports most conversions between Spark SQL and Avro records, making Avro a first-class citizen in Spark. 0+ with python 3. But it all requires if you move from spark shell to IDE. So let’s start. Code is run in a spark-shell. GitHub Gist: instantly share code, notes, and snippets. Use the following steps to save this file to a project in Cloudera Data Science Workbench, and then load it into a table in Apache Impala. In this part, we will create an AWS Glue job that uses an S3 bucket as a source and AWS SQL Server RDS database as a target. Use the following commands to create a DataFrame (df) and read a JSON document named employee. Let the job run for a while and you should see the data being written to the S3 directory specified in the streaming class. Develop Spark/MapReduce jobs to parse the JSON or XML data. runQuery is a Scala function in Spark connector and not the Spark Standerd API. The Python objects representing the ACL can be found in the acl. >>> from pyspark import SparkContext >>> sc = SparkContext(master. jsonFile(“/path/to/myDir”) is deprecated from spark 1. My Lambda function reads CSV file content, then send an email with the file content and info. This time we are having the same sample JSON data. Below is what I have learned thus far. without requiring a new build. Learn how to read data from Apache columnar file format that provides optimizations to speed up queries and is a far more efficient file format than CSV or JSON. HDFS has several advantages over S3, however, the cost/benefit for running long running HDFS clusters on AWS vs. Also the notebooks from S3 are read-only in Zepl and need to be cloned to modify. An Amazon S3 bucket is a storage location to hold files. tools Ternary Operators Ternary operators are shortcut for an if-else statement, and are also known as a conditional operators. They are extracted from open source Python projects. via builtin open function) or StringIO. There are many reasons to like Anaconda, but the important things here are that it can be installed without administrator rights, supports all three major operating systems, and provides all of the packages needed for working with KNIME “out of the box”. dockercfg file created by the "docker login" command into the S3 bucket specified in the Dockerrun. XML, JSON, YML in Python Use Cisco Spark to communicate with the speaker after the session 1. See Also-. It works as a request-response protocol between a client and server. You can store your data in S3, then read and process it without actually storing it in your nodes and after processing it through spark you can write it back to S3 and terminate EMR. gov for traffic violations. Storing JSON in text columns. The JSON Lines format has three requirements: 1. In this blog, I will be covering the processing of JSON from HDFS only. HBase Thrift. We use cookies for various purposes including analytics. Question Cagtegory: Apache-spark Filter by Select Categories Android AngularJs Apache-spark Arrays Azure Bash Bootstrap c C# c++ CSS Database Django Excel Git Hadoop HTML / CSS HTML5 Informatica iOS Java Javascript Jenkins jQuery Json knockout js Linux Meteor MongoDB Mysql node. The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to support and facilitate the growth of a diverse and international community of Python programmers. Importantly, in the context of this article, the handler must set the LD_LIBRARY_PATH to point to any shared libraries that the worker may need. CHAOSSEARCH can natively analyze the NDJSON format, so after uploading the data to Amazon S3 I was able to index it in just a few minutes. or just chat with the experts at Google who help build the support. PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. As an example, we will look at Durham police crime reports from the Dhrahm Open Data website. Likewise in JSON Schema, for anything but the most trivial schema, it’s really useful to structure the schema into parts that can be reused in a number of places. And since Arrow is so closely related to parquet-cpp, support for Parquet output (again, from Python) is baked-in. sql import SparkSession >>> spark = SparkSession \. Here is the sample JSON definition of a Spark Activity:. It can express information like XML. 03/15/2018; 2 minutes to read +1; In this article. (Last Updated On: June 26, 2018) I have been experimenting with Apache Avro and Python. We are going to load a JSON input source to Spark SQL's SQLContext. The Spark context (often named sc) has methods for creating RDDs and is responsible for making RDDs resilient and distributed. Read and Write DataFrame from Database using PySpark. Designed as an efficient way to navigate the intricacies of the Spark ecosystem, Sparkour aims to be an approachable, understandable, and actionable cookbook for distributed data processing. JSON is a text format that is completely language-independent but uses conventions that are familiar to programmers of the C family of languages, including C, C++, C#, Java, JavaScript. parquet placed in the same directory where spark-shell is running. This Spark SQL tutorial with JSON has two parts. The following are code examples for showing how to use pyspark. com DataCamp Learn Python for Data Science Interactively Initializing SparkSession Spark SQL is Apache Spark's module for working with structured data. With Spark, you can get started with big data processing, as it has built-in modules for streaming, SQL, machine learning and graph processing. Spark is built on the concept of distributed datasets, which contain arbitrary Java or Python objects. CHAOSSEARCH can natively analyze the NDJSON format, so after uploading the data to Amazon S3 I was able to index it in just a few minutes. 2, “Functions That Create JSON Values”) as well as by casting values of other types to the JSON type using CAST(value AS JSON) (see Converting between JSON and non-JSON values). It abstracts the complexities of making requests behind a beautiful, simple API so that you can focus on interacting with services and consuming data in your application. When dealing with vast amounts of data, a common problem is that a small amount of the data is malformed or corrupt. alph486 changed the title read_json(lines=True) broken for s3 urls in Python 3 read_json(lines=True) broken for s3 urls in Python 3 (v0. 0 (April 2015) • Runs SQL / HiveQL queries, optionally alongside or replacing existing Hive deployments. py module of boto. Formats may range the formats from being the unstructured, like text, to semi structured way, like JSON, to structured, like Sequence Files. Amazon S3 or Amazon Simple Storage Service is a service offered by Amazon Web Services (AWS) that provides object storage through a web service interface. Easily organize, use, and enrich data — in real time, anywhere. Also the notebooks from S3 are read-only in Zepl and need to be cloned to modify. SparkConf(). how to read multi-line json in spark. Plus, with the evident need for handling complex analysis and munging tasks for Big Data, Python for Spark or PySpark Certification has become one of the most sought-after skills in the industry today. Using Hive as data store we can able to load JSON data into Hive tables by creating schemas. Bakker , August 21, 2016 0 5 min read Lately I’ve been playing around with USB led lights in. ORC format was introduced in Hive version 0. Also, S3 data (. Spark is built on the concept of distributed datasets, which contain arbitrary Java or Python objects. • Spark is a general-purpose big data platform. • MLlib is a standard component of Spark providing machine learning primitives on top of Spark. There are no any problems. JSON TO HIVE TABLE. simple is a simple Java toolkit for JSON. • Reads from HDFS, S3, HBase, and any Hadoop data source. Spark runs programs up to 100x faster than Apache Hadoop MapReduce in memory, or 10x faster on disk. vega_embed to render charts from Vega and Vega-Lite specifications. 0 and later, you can use S3 Select with Spark on Amazon EMR. Save the code as file parse_json. The abbreviation of JSON is JavaScript Object Notation. In this tutorial, we'll convert Python dictionary to JSON and write it to a text file. In the first example, the script builds a list of tuples, with each row in the database becoming one tuple. Python For Data Science Cheat Sheet Pandas Basics Learn Python for Data Science Interactively at www. ObjectMapper is most important class which acts as codec or data binder. Requirements. In this post, we introduce the Snowflake Connector for Spark (package available from Maven Central or Spark Packages, source code in Github) and make the case for using it to bring Spark and Snowflake together to power your data-driven solutions. Power BI, Tableau, Qlik, SSRS, MicroStrategy, Excel, MS Access), ETL Tools (i. The most important and primary step in Data Analysis is gathering data from all possible sources (Primary or Secondary). To explicitly force Series parsing, pass typ=series. PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. Then, we'll read in back from the file and play with it. In this post, we introduce some of the new YAML features. If your cluster is running Databricks Runtime 4. toJavaRDD(). MLflow Models. He loves Open source technologies and writing on JournalDev has become his passion. tools Ternary Operators Ternary operators are shortcut for an if-else statement, and are also known as a conditional operators. Read, Enrich and Transform Data with AWS Glue Service. SQLContext(sc) Example. We will run through the following steps: creating a simple batch job that reads data from Cassandra and writes the result in parquet in S3. We'll start with something simple. For alternatives, please see the Advanced Options section. For all file types, you read the files into a DataFrame and write out in delta format: Python. This makes parsing JSON files significantly easier than before. We will run through the following steps: creating a simple batch job that reads data from Cassandra and writes the result in parquet in S3. The data will parse using data frame. 0 and above. AWS storage credentials stored in the account are used to retrieve the script file. When using spark, we often need to check whether a hdfs path exist before load the data, as if the path is not valid, we will get the following exception:org. The JSON JDBC Driver enables users to connect with live JSON services, directly from any applications that support JDBC connectivity. XML Source / JSON Source both can parse API response into Rows and Columns so you can easily store it into SQL. In this tutorial, we shall learn to write a Spark Application in Python Programming Language and submit the application to run in Spark with local input and minimal (no) options. 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. Keys can show up in logs and table metadata and are therefore fundamentally insecure. The number of partitions and the time taken to read the file are read from the Spark UI. In this article, we will focus on how to use Amazon S3 for regular file handling operations using Python and Boto library. Review the App Engine Standard Environment Cloud Storage Sample for an example of how to use Cloud Storage in App Engine Standard environment for Python 2. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. 12) Now we can import the module by running import openpyxl. Secure 'Serverless' File Uploads with AWS Lambda, S3, and Zappa Deploy a Python-based Zappa microservice onto AWS Lambda to facilitate direct browser to S3 file uploads. through a standard ODBC Driver interface. Spark SQL can automatically infer the schema of a JSON dataset, and use it to load data into a DataFrame object. SparkR can connect to S3. parquet, etc. With the new Python integration in KNIME you can use an existing Python installation from KNIME. The following example illustrates how to read a text file from Amazon S3 into an RDD, convert the RDD to a DataFrame, and then use the Data Source API to write the DataFrame into a Parquet file on Amazon S3: Specify Amazon S3 credentials. AWS supports a number of languages including NodeJS, C#, Java, Python and many more that can be used to access and read file. In Spark, JSON can be processed from different Data Storage layers like Local, HDFS, S3, RDBMS or NoSQL. Before getting started, it is important that you understand Spark terminology and workflow, system requirements and support, and OJAI connector and API features. json(“/path/to/myDir”) or spark. This Spark SQL tutorial with JSON has two parts. textFile() method. Python File Handling Python Read Files Python Write/Create Files Python Delete Files Python MySQL MySQL Get Started MySQL Create Database MySQL Create Table MySQL Insert MySQL Select MySQL Where MySQL Order By MySQL Delete MySQL Drop Table MySQL Update MySQL Limit MySQL Join Python MongoDB. Visualizing Amazon SQS and S3 using Python and Dremio. Read, Enrich and Transform Data with AWS Glue Service. The first part shows examples of JSON input sources with a specific structure. Code 1: Reading Excel pdf = pd. We’ll mine big data to find relationships between movies, recommend movies, analyze social graphs of super-heroes, detect spam emails, search Wikipedia, and much more!. 11+ Features. Big Data Hadoop Architect Masters Program. When using local file APIs, you must provide the path under /dbfs. S3 is NOT a file system. Read access keys from ~/. a new file created in an S3 bucket), perform any amazon-specific tasks (like fetching data from s3) and invoke the worker. If this doesn’t work, verify your installation location as in the screenshot above. Validate JSON data against a JSON Schema from the Command Line. Converting Python data to JSON is called an Encoding operation. Python For Data Science Cheat Sheet PySpark - RDD Basics Learn Python for data science Interactively at www. The dataset contains data in JSON format about United States legislators and the seats that they have held in the US House of Representatives and Senate, and has been modified slightly and made available in a public Amazon S3 bucket for purposes of this tutorial. A data scientist works with text, csv and excel files frequently. wholeTextFiles("/path/to/dir") to get an. scala> val sqlcontext = new org. However, making them play nicely together is no simple task. UTF-8 Encoding. The example I did was a very basic one - simple counts of inbound tweets and grouping by user. • Spark is a general-purpose big data platform. Suppose we have a dataset which is in CSV format. WARN_RECIPE_SPARK_INDIRECT_S3: No direct access to read/write S3 dataset; Python APIs » API for managed folders read_json (filename) ¶ Reads a JSON file. Spark makes processing of JSON easy via SparkSQL API using SQLContext object (org. Fast Data Analytics with Spark and Python (PySpark) District Data Labs 2. For example, an object that uses memoization to cache the results of expensive computations could still be considered an immutable object. 03/11/2019; 7 minutes to read +6; In this article. databricks-utils is a python package that provide several utility classes/func that improve ease-of-use in databricks notebook. Here’s a snippet of the python code that is similar to the scala code, above. With Spark, you can get started with big data processing, as it has built-in modules for streaming, SQL, machine learning and graph processing. dump method. To create a DataFrame, first create a SparkSession object, then use the object’s createDataFrame() function. In practice, the cluster will be hosted on a remote machine that's connected to all other nodes. 0 and above. Spark SQL 3 Improved multi-version support in 1. To reference variables in other JSON files use the ${file(. json("/path/to/myDir") or spark. appName("Python Spark SQL basic example") \. ORC format was introduced in Hive version 0. S3Bucket class to easily interact with a S3 bucket via dbfs and databricks spark. Get the Redshift COPY command guide as PDF! About COPY Command; COPY command syntax; COPY sample commands. If you are looking for an IPython version compatible with Python 2. I have start and end dates. 2, “Functions That Create JSON Values”) as well as by casting values of other types to the JSON type using CAST(value AS JSON) (see Converting between JSON and non-JSON values). Uploading JSON files to DynamoDB from Python Posting JSON to DynamoDB through the AWS CLI can fail due to Unicode errors, so it may be worth importing your data manually through Python. gov for traffic violations. com uses to run its global e-commerce network. Going a step further, we could use tools that can read data in JSON format. Spark Framework is a simple and expressive Java/Kotlin web framework DSL built for rapid development. Write a Spark DataFrame to a Parquet file. The requirement is to load JSON Data into Hive Partitioned table using Spark. wholeTextFiles(“/path/to/dir”) to get an. S3 Select supports select on. With Spark, only one-step is needed where data is read into memory, operations performed, and the results written back—resulting in a much faster execution. The first step in using Spark is connecting to a cluster. Learn more. All traffic is encrypted (for free) with HTTPS/SSL!. functions therefore we will start off by importing that. A community forum to discuss working with Databricks Cloud and Spark. encoding and errors guide pickle with decoding 8-bit string instances pickled by Python 2. Here is an example of writing a. 1 comment on"Submit Spark jobs via REST in IOP 4. Reading and Writing the Apache Parquet Format¶. Example, I'm downloaded a json file from catalog. ly is the comprehensive content analytics platform for web, mobile, and other channels. Designed as an efficient way to navigate the intricacies of the Spark ecosystem, Sparkour aims to be an approachable, understandable, and actionable cookbook for distributed data processing. Loading and Saving Data in Spark. It is available so that developers that use older versions of Python can use the latest features available in the json lib. ElasticSearch Spark is a connector that existed before 2. This Spark SQL JSON with Python tutorial has two parts. How to parse read multiline json files in spark spark read json string java, spark read json string python, spark read json from s3, parsing json in spark-streaming, spark dataframe nested json. Python For Data Science Cheat Sheet Pandas Basics Learn Python for Data Science Interactively at www. Involved in HBASE setup and storing data into HBASE, which will be used for analysis. If this doesn’t work, verify your installation location as in the screenshot above. OK, I Understand. We'll start with something simple. AWS supports a number of languages including NodeJS, C#, Java, Python and many more that can be used to access and read file. This tutorial shows you how to connect your Azure Databricks cluster to data stored in an Azure storage account that has Azure Data Lake Storage Gen2 enabled. Python Logging Setup 1. json file and uploaded it during the creation of my Beanstalk (docker) environment. spark kotlin csv nas. uint:n n bits as an unsigned integer. SparkSession(). 5, with more than 100 built-in functions introduced in Spark 1. Installing Spark on Windows 10. py module of boto. In this article we will focus on how to use Amzaon S3 for regular file handling operations using Python and Boto library. The S3 bucket has two folders. Spark SQL is a Spark module for structured data processing. The requirement is to load JSON Data into Hive Partitioned table using Spark. We’ll mine big data to find relationships between movies, recommend movies, analyze social graphs of super-heroes, detect spam emails, search Wikipedia, and much more!. Log files), and it seems to run a lot faster. functions therefore we will start off by importing that. All traffic is encrypted (for free) with HTTPS/SSL!. json(rdd) to make spark infer the schema from json string inside rdd. File Formats : Spark provides a very simple manner to load and save data files in a very large number of file formats. Using Amazon Elastic Map Reduce (EMR) with Spark and Python 3. 0 (April 2015) • Runs SQL / HiveQL queries, optionally alongside or replacing existing Hive deployments. Get started quickly using AWS with boto3, the AWS SDK for Python. ObjectMapper can write java object into JSON file and read JSON file into java Object. x delivers notable improvements in the areas of API, schema awareness, Performance, Structured. Understanding Apache Spark. SSIS Components and Tasks for REST API, JSON, XML, Amazon, Azure, MongoDB, Google Analytics, Redshift, DynamoDB, Amazon S3, SOAP, Salesforce, Dynamics CRM. The first part shows examples of JSON input sources with a specific structure. I'm trying to utilize Spark/PySpark (version 1. Spark Streaming using TCP Socket. This is the first post in a 2-part series describing Snowflake's integration with Spark. So, I was looking to write a simple AWS Lambda function in Python. Not only can the json. JSON is an acronym standing for JavaScript Object Notation. The pandas read_json() function can create a pandas Series … - Selection from Python Data Analysis [Book]. Spark SQL JSON with Python Overview. Simple example of processing twitter JSON payload from a Kafka stream with Spark Streaming in Python - 01_Spark+Streaming+Kafka+Twitter. xlsx) sparkDF = sqlContext. python·s3 ·salesforce· How read 100GB json string in spark??. tools Ternary Operators Ternary operators are shortcut for an if-else statement, and are also known as a conditional operators. There are a few things. The first will deal with the import and export of any type of data, CSV , text file…. Python and more. We examine how Structured Streaming in Apache Spark 2. Leverage the pyodbc module for ODBC in Python. py The following screenshot is captured from my local environment (Spark 2. In this article, we've given you a simple guide to using JSON in your programs, including how to create and parse JSON, and how to access data locked inside it. Store an object in S3 using the name of the Key object as the key in S3 and the contents of the file pointed to by ‘fp’ as the contents. October 15, 2015 How To Parse and Convert JSON to CSV using Python May 20, 2016 How To Parse and Convert XML to CSV using Python November 3, 2015 Use JSPDF for Exporting Data HTML as PDF in 5 Easy Steps July 29, 2015 How To Manage SSH Keys Using Ansible August 26, 2015 How To Write Spark Applications in Python. This library requires. Spark is often an order(s) of magnitude faster than Hadoop for. jsonFile("/path/to/myDir") is deprecated from spark 1. through a standard ODBC Driver interface. The string could be a URL. In the following example, we do just that and then print out the data we got:. ElasticSearch Spark is a connector that existed before 2. The newsletter is offered in English only at the moment. simplejson mimics the json standard library. simple is a simple Java toolkit for JSON. They are extracted from open source Python projects. This guide is maintained on GitHub by the Python Packaging Authority. With the challenge of type casting requirements in the API, you would still not the required type safety and will make your code brittle. Get started working with Python, Boto3, and AWS S3. I wish to use AWS lambda python service to parse this JSON and send the parsed results to an AWS RDS MySQL database. UTF-8 Encoding. We don't yet support dbutils in R, but maybe you can open a notebook in Python or Scala to run this simple command. Fusion Professionals is a dynamic IT services company based in Sydney, Australia. How do I parse a JSON file in python to read data from it?. Plus, with the evident need for handling complex analysis and munging tasks for Big Data, Python for Spark or PySpark Certification has become one of the most sought-after skills in the industry today. I have start and end dates. Used Spark-SQL to Load JSON data and create Schema RDD and loaded it into Hive Tables and handled Structured data using SparkSQL. Apache Spark Examples. AWS storage credentials stored in the account are used to retrieve the script file. The core data structure in Spark is an RDD, or a resilient distributed dataset. In this first blog post in the series on Big Data at Databricks, we explore how we use Structured Streaming in Apache Spark 2. A community forum to discuss working with Databricks Cloud and Spark. They are extracted from open source Python projects. This post describes the use of Blaze and Impala on a Hadoop cluster. 4; File on S3 was created from Third Party – See Reference Section below for specifics on how the file was created. Spark's new DataFrame API is inspired by data frames in R and Python (Pandas), but designed from the ground up to support modern big data and data science applications. Before you can start working with JSON in Python, you'll need some JSON to work with. Using lambda with s3 and dynamodb:. The CHAOS SEARCH platform doesn’t require users to set up index schemas or define mappings for their data, so it discovered all of the fields—strings, integers, etc. There will be one computer, called the master that manages splitting up the data and the computations. DevOps Engineers Rejoice! AWS’s new support for YAML format is a big improvement for the developer experience. Not only can the json. Read access keys from ~/. json("/path/to/myDir") or spark. For example, my new role's name is lambda-with-s3-read. The main way developers are productive is by composing existing libraries 3. SparkConf(). Spark SQL - Quick Guide - Industries are using Hadoop extensively to analyze their data sets. Spark SQL supports many built-in transformation functions in the module pyspark. It abstracts the complexities of making requests behind a beautiful, simple API so that you can focus on interacting with services and consuming data in your application. JSON is widely used in web applications or as server response because it’s lightweight and more compact than XML. In this first blog post in the series on Big Data at Databricks, we explore how we use Structured Streaming in Apache Spark 2. Then, we'll read in back from the file and play with it. Read, Enrich and Transform Data with AWS Glue Service. json(“/path/to/myDir”) or spark. A community forum to discuss working with Databricks Cloud and Spark. Loading JSON data using SparkSQL. In this part, we will create an AWS Glue job that uses an S3 bucket as a source and AWS SQL Server RDS database as a target. I used spark. The above APIs can be used to read data from Amazon S3 data store and convert them into a DataFrame or RDD, and write the content of the DataFrame or RDD to Amazon S3 data store. Configure S3 filesystem support for Spark on OSX. csv to this folder. Learn Python for data science Interactively at www. We will use a JSON lookup file to enrich our data during the AWS Glue transformation. Each line must contain a separate, self-contained. Composable Parallel Processing in Apache Spark and Weld 1. Transforming Complex Data Types in Spark SQL. Spark uses libraries from Hadoop to connect to S3, and the integration between Spark, Hadoop, and the AWS services can feel a little finicky. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: