Configure Simba ODBC driver with a proxy in Windows

In this article you learn how to configure the Databricks ODBC Driver when your local Windows machine is behind a proxy server. Download the Simba driver for Windows Download and install the latest version of the Databricks ODBC Driver for Windows. Add proxy settings to the Windows registry Open the Windows registry and add the proxy settings to the...

Troubleshooting JDBC and ODBC connections

This article provides information to help you troubleshoot the connection between your Databricks JDBC/ODBC server and BI tools and data sources. Fetching result set is slow after statement execution After a query execution, you can fetch result rows by calling the next() method on the returned ResultSet repeatedly. This method triggers a request to...

Install a private PyPI repo

Certain use cases may require you to install libraries from private PyPI repositories. If you are installing from a public repository, you should review the library documentation. This article shows you how to configure an example init script that authenticates and downloads a PyPI library from a private repository. Create init script Create (or ver...

Cannot apply updated cluster policy

Problem You are attempting to update an existing cluster policy, however the update does not apply to the cluster associated with the policy. If you attempt to edit a cluster that is managed by a policy, the changes are not applied or saved. Cause This is a known issue that is being addressed. Solution You can use a workaround until a permanent fix ...

Cluster Apache Spark configuration not applied

Problem Your cluster’s Spark configuration values are not applied. Cause This happens when the Spark config values are declared in the cluster configuration as well as in an init script. When Spark config values are located in more than one place, the configuration in the init script takes precedence and the cluster ignores the configuration setting...

Admin user cannot restart cluster to run job

Problem When a user who has permission to start a cluster, such as a Databricks Admin user, submits a job that is owned by a different user, the job fails with the following message: Message: Run executed on existing cluster ID <cluster id> failed because of insufficient permissions. The error received from the cluster manager was: 'You are no...

Cluster fails to start with dummy does not exist error

Problem You try to start a cluster, but it fails to start. You get an Apache Spark error message. Internal error message: Spark error: Driver down You review the cluster driver and worker logs and see an error message containing java.io.FileNotFoundException: File file:/databricks/driver/dummy does not exist. 21/07/14 21:44:06 ERROR DriverDaemon$: X...

Cluster slowdown due to Ganglia metrics filling root partition

Note This article applies to Databricks Runtime 7.3 LTS and below. Problem Clusters start slowing down and may show a combination of the following symptoms: Unhealthy cluster events are reported: Request timed out. Driver is temporarily unavailable. Metastore is down. DBFS is down. You do not see any high GC events or memory utilization associated w...

Failed to create cluster with invalid tag value

Problem You are trying to create a cluster, but it is failing with an invalid tag value error message. System.Exception: Content={"error_code":"INVALID_PARAMETER_VALUE","message":"\nInvalid tag value (<<<<TAG-VALUE>>>>) - the length cannot exceed 256\nUnicode characters in UTF-8.\n "} Cause Limitations on tag Key and Value ar...

Set executor log level

Warning This article describes steps related to customer use of Log4j 1.x within a Databricks cluster. Log4j 1.x is no longer maintained and has three known CVEs (CVE-2021-4104, CVE-2020-9488, and CVE-2019-17571). If your code uses one of the affected classes (JMSAppender or SocketServer), your use may potentially be impacted by these vulnerabilitie...

Auto termination is disabled when starting a job cluster

Problem You are trying to start a job cluster, but the job creation fails with an error message. Error creating job Cluster autotermination is currently disabled. Cause Job clusters auto terminate once the job is completed. As a result, they do not support explicit autotermination policies. If you include autotermination_minutes in your cluster poli...

How to overwrite log4j configurations on Databricks clusters

Warning This article describes steps related to customer use of Log4j 1.x within a Databricks cluster. Log4j 1.x is no longer maintained and has three known CVEs (CVE-2021-4104, CVE-2020-9488, and CVE-2019-17571). If your code uses one of the affected classes (JMSAppender or SocketServer), your use may potentially be impacted by these vulnerabilitie...

Apache Spark executor memory allocation

By default, the amount of memory available for each executor is allocated within the Java Virtual Machine (JVM) memory heap. This is controlled by the spark.executor.memory property. However, some unexpected behaviors were observed on instances with a large amount of memory allocated. As JVMs scale up in memory size, issues with the garbage collecto...

Configure a cluster to use a custom NTP server

By default Databricks clusters use public NTP servers. This is sufficient for most use cases, however you can configure a cluster to use a custom NTP server. This does not have to be a public NTP server. It can be a private NTP server under your control. A common use case is to minimize the amount of Internet traffic from your cluster. Update the NT...

Enable GCM cipher suites

Databricks clusters do not have GCM (Galois/Counter Mode) cipher suites enabled by default. You must enable GCM cipher suites on your cluster to connect to an external server that requires GCM cipher suites. Verify required cipher suites Use the nmap utility to verify which cipher suites are required by the external server. %sh nmap --script ssl-enu...

Enable retries in init script

Init scripts are commonly used to configure Databricks clusters. There are some scenarios where you may want to implement retries in an init script. Example init script This sample init script shows you how to implement a retry for a basic copy operation. You can use this sample code as a base for implementing retries in your own init script. %scala...

Append to a DataFrame

To append to a DataFrame, use the union method. %scala val firstDF = spark.range(3).toDF("myCol") val newRow = Seq(20) val appended = firstDF.union(newRow.toDF()) display(appended) %python firstDF = spark.range(3).toDF("myCol") newRow = spark.createDataFrame([[20]]) appended = firstDF.union(newRow) display(appended)...

How to improve performance with bucketing

Bucketing is an optimization technique in Apache Spark SQL. Data is allocated among a specified number of buckets, according to values derived from one or more bucketing columns. Bucketing improves performance by shuffling and sorting data prior to downstream operations such as table joins. The tradeoff is the initial overhead due to shuffling and s...

How to handle blob data contained in an XML file

If you log events in XML format, then every XML event is recorded as a base64 string. In order to run analytics on this data using Apache Spark, you need to use the spark_xml library and the BASE64DECODER API to transform the data for analysis. Problem You need to analyze base64-encoded strings from an XML-formatted log file using Spark. For example...

Simplify chained transformations

Sometimes you may need to perform multiple transformations on your DataFrame: %scala import org.apache.spark.sql.functions._ import org.apache.spark.sql.DataFrame val testDf = (1 to 10).toDF("col") def func0(x: Int => Int, y: Int)(in: DataFrame): DataFrame = {   in.filter('col > x(y)) } def func1(x: Int)(in: DataFrame): DataFrame = {   in.sele...

Hive UDFs

This article shows how to create a Hive UDF, register it in Spark, and use it in a Spark SQL query. Here is a Hive UDF that takes a long as an argument and returns its hexadecimal representation. %scala import org.apache.hadoop.hive.ql.exec.UDF import org.apache.hadoop.io.LongWritable // This UDF takes a long integer and converts it to a hexadecimal...

Prevent duplicated columns when joining two DataFrames

If you perform a join in Spark and don’t specify your join correctly you’ll end up with duplicate column names. This makes it harder to select those columns. This article and notebook demonstrate how to perform a join so that you don’t have duplicated columns. Join on columns If you join on columns, you get duplicated columns. Scala %scala val llist...

Revoke all user privileges

When user permissions are explicitly granted for individual tables and views, the selected user can access those tables and views even if they don’t have permission to access the underlying database. If you want to revoke a user’s access, you can do so with the REVOKE command. However, the REVOKE command is explicit, and is strictly scoped to the ob...

How to list and delete files faster in Databricks

Scenario Suppose you need to delete a table that is partitioned by year, month, date, region, and service. However, the table is huge, and there will be around 1000 part files per partition. You can list all the files in each partition and then delete them using an Apache Spark job. For example, suppose you have a table that is partitioned by a, b, ...

How to handle corrupted Parquet files with different schema

Problem Let’s say you have a large list of essentially independent Parquet files, with a variety of different schemas. You want to read only those files that match a specific schema and skip the files that don’t match. One solution could be to read the files in sequence, identify the schema, and union the DataFrames together. However, this approach ...

No USAGE permission on database

Problem You are using a cluster running Databricks Runtime 7.3 LTS and above. You have enabled table access control for your workspace (AWS | Azure | GCP) as the admin user, and granted the SELECT privilege to a standard user-group that needs to access the tables. A user tries to access an object in the database and gets a SecurityException error me...

Nulls and empty strings in a partitioned column save as nulls

Problem If you save data containing both empty strings and null values in a column on which the table is partitioned, both values become null after writing and reading the table. To illustrate this, create a simple DataFrame: %scala import org.apache.spark.sql.types._ import org.apache.spark.sql.catalyst.encoders.RowEncoder val data = Seq(Row(1, "")...

Behavior of the randomSplit method

When using randomSplit on a DataFrame, you could potentially observe inconsistent behavior. Here is an example: %python df = spark.read.format('inconsistent_data_source').load() a,b = df.randomSplit([0.5, 0.5]) a.join(broadcast(b), on='id', how='inner').count() Typically this query returns 0. However, depending on the underlying data source or input...

Generate schema from case class

Spark provides an easy way to generate a schema from a Scala case class. For case class A, use the method ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType]. For example: %scala import org.apache.spark.sql.types.StructType import org.apache.spark.sql.catalyst.ScalaReflection case class A(key: String, time: java.sql.Timestamp, date: java....

How to specify skew hints in dataset and DataFrame-based join commands

When you perform a join command with DataFrame or Dataset objects, if you find that the query is stuck on finishing a small number of tasks due to data skew, you can specify the skew hint with the hint("skew") method: df.hint("skew"). The skew join optimization (AWS | Azure | GCP) is performed on the DataFrame for which you specify the skew hint. In...

How to update nested columns

Spark doesn’t support adding new columns or dropping existing columns in nested structures. In particular, the withColumn and drop methods of the Dataset class don’t allow you to specify a column name different from any top level columns. For example, suppose you have a dataset with the following schema: %scala val schema = (new StructType)       .a...

Incompatible schema in some files

Problem The Spark job fails with an exception like the following while reading Parquet files: Error in SQL statement: SparkException: Job aborted due to stage failure: Task 20 in stage 11227.0 failed 4 times, most recent failure: Lost task 20.3 in stage 11227.0 (TID 868031, 10.111.245.219, executor 31): java.lang.UnsupportedOperationException: org.a...

Create tables on JSON datasets

In this article we cover how to create a table on JSON datasets using SerDe. Download the JSON SerDe JAR Open the hive-json-serde 1.3.8 download page. Click on json-serde-1.3.8-jar-with-dependencies.jar to download the file json-serde-1.3.8-jar-with-dependencies.jar. Info You can review the Hive-JSON-Serde GitHub repo for more information on the JAR...

Optimize read performance from JDBC data sources

Problem Reading data from an external JDBC database is slow. How can I improve read performance? Solution See the detailed discussion in the Databricks documentation on how to optimize performance when reading data (AWS | Azure | GCP) from an external JDBC database....

Failure to detect encoding in JSON

Problem Spark job fails with an exception containing the message: Invalid UTF-32 character 0x1414141(above 10ffff)  at char #1, byte #7) At org.apache.spark.sql.catalyst.json.JacksonParser.parse Cause The JSON data source reader is able to automatically detect encoding of input JSON files using BOM at the beginning of the files. However, BOM is not ...

Inconsistent timestamp results with JDBC applications

Problem When using JDBC applications with Databricks clusters you see inconsistent java.sql.Timestamp results when switching between standard time and daylight saving time. Cause Databricks clusters use UTC by default. java.sql.Timestamp uses the JVM’s local time zone. If a Databricks cluster returns 2021-07-12 21:43:08 as a string, the JVM parses i...

Kafka client terminated with OffsetOutOfRangeException

Problem You have an Apache Spark application that is trying to fetch messages from an Apache Kafka source when it is terminated with a kafkashaded.org.apache.kafka.clients.consumer.OffsetOutOfRangeException error message. Cause Your Spark application is trying to fetch expired data offsets from Kafka. We generally see this in these two scenarios: Sc...

Null column values display as NaN

Problem You have a table with null values in some columns. When you query the table using a select statement in Databricks, the null values appear as null. When you query the table using the same select statement in Databricks SQL, the null values appear as NaN. %sql select * from default.<table-name> where <column-name> is null Databric...

Retrieve queries owned by a disabled user

When a Databricks SQL user is removed from an organization, the queries owned by the user remain, but they are only visible to those who already have permission to access them. A Databricks SQL admin can transfer ownership to other users, as well as delete alerts, dashboards, and queries owned by the disabled user account. Clone a query A Databricks...

Apache Spark session is null in DBConnect

Problem You are trying to run your code using Databricks Connect ( AWS  |  Azure  |  GCP ) when you get a sparkSession is null error message. java.lang.AssertionError: assertion failed: sparkSession is null while trying to executeCollectResult at scala.Predef$.assert(Predef.scala:170) at org.apache.spark.sql.execution.SparkPlan.executeCollectResult(...

Failed to create process error with Databricks CLI in Windows

Problem While trying to access the Databricks CLI (AWS | Azure | GCP) in Windows, you get a failed to create process error message. Cause This can happen: If multiple instances of the Databricks CLI are installed on the system. If the Python path on your Windows system includes a space. Info There is a known issue in pip which causes pip installed s...

GeoSpark undefined function error with DBConnect

Problem You are trying to use the GeoSpark function st_geofromwkt with DBConnect (AWS | Azure | GCP) and you get an Apache Spark error message. Error: org.apache.spark.sql.AnalysisException: Undefined function: 'st_geomfromwkt'. This function is neither a registered temporary function nor a permanent function registered in the database 'default'.; T...

Get Apache Spark config in DBConnect

You can always view the Spark configuration (AWS | Azure | GCP) for your cluster by reviewing the cluster details in the workspace. If you are using DBConnect (AWS | Azure | GCP) you may want to quickly review the current Spark configuration details without switching over to the workspace UI. This example code shows you how to get the current Spark ...

ProtoSerializer stack overflow error in DBConnect

Problem You are using DBConnect (AWS | Azure | GCP) to run a PySpark transformation on a DataFrame with more than 100 columns when you get a stack overflow error. py4j.protocol.Py4JJavaError: An error occurred while calling o945.count. : java.lang.StackOverflowError     at java.lang.Class.getEnclosingMethodInfo(Class.java:1072)     at java.lang.Clas...

Use tcpdump to create pcap files

If you want to analyze the network traffic between nodes on a specific cluster, you can install tcpdump on the cluster and use it to dump the network packet details to pcap files. The pcap files can then be downloaded to a local machine for analysis. Create the tcpdump init script Run this sample script in a notebook on the cluster to create the ini...

Terraform registry does not have a provider error

Problem You are installing the Databricks Terraform provider ( AWS | Azure | GCP) and get a Databricks provider registry error. Error while installing hashicorp/databricks: provider registry registry.terraform.io does not have a provider named registry.terraform.io/hashicorp/databricks Cause This error occurs when the required_providers block is not...

Compare two versions of a Delta table

Delta Lake supports time travel, which allows you to query an older snapshot of a Delta table. One common use case is to compare two versions of a Delta table in order to identify what changed. For more details on time travel, please review the Delta Lake time travel documentation (AWS | Azure | GCP). Identify all differences You can use a SQL SELEC...

Delta Merge cannot resolve nested field

Problem You are attempting a Delta Merge with automatic schema evolution, but it fails with a Delta Merge: cannot resolve 'field' due to data type mismatch error message. Cause This can happen if you have made changes to the nested column fields. For example, assume we have a column called Address with the fields streetName, houseNumber, and city ne...

How Delta cache behaves on an autoscaling cluster

This article is about how Delta cache (AWS | Azure | GCP) behaves on an auto-scaling cluster, which removes or adds nodes as needed. When a cluster downscales and terminates nodes: A Delta cache behaves in the same way as an RDD cache. Whenever a node goes down, all of the cached data in that particular node is lost. Delta cache data is not moved fr...

How to improve performance of Delta Lake MERGE INTO queries using partition pruning

This article explains how to trigger partition pruning in Delta Lake MERGE INTO (AWS | Azure | GCP) queries from Databricks. Partition pruning is an optimization technique to limit the number of partitions that are inspected by a query. Discussion MERGE INTO is an expensive operation when used with Delta tables. If you don’t partition the underlying...

Best practices for dropping a managed Delta Lake table

Regardless of how you drop a managed table, it can take a significant amount of time, depending on the data size. Delta Lake managed tables in particular contain a lot of metadata in the form of transaction logs, and they can contain duplicate data files. If a Delta table has been in use for a long time, it can accumulate a very large amount of data...

How to populate or update columns in an existing Delta table

Problem You have an existing Delta table, with a few empty columns. You need to populate or update those columns with data from a raw Parquet file. Solution In this example, there is a customers table, which is an existing Delta table. It has an address column with missing values. The updated data exists in Parquet format. Create a DataFrame from th...

Identify duplicate data on append operations

A common issue when performing append operations on Delta tables is duplicate data. For example, assume user 1 performs a write operation on Delta table A. At the same time, user 2 performs an append operation on Delta table A. This can lead to duplicate records in the table. In this article, we review basic troubleshooting steps that you can use to...

Optimize a Delta sink in a structured streaming application

You are using a Delta table as the sink for your structured streaming application and you want to optimize the Delta table so that queries are faster. If your structured streaming application has a very frequent trigger interval, it may not create sufficient files that are eligible for compaction in each microbatch. The autoOptimize operation compac...

Unable to cast string to varchar

Problem You are trying to cast a string type column to varchar but it isn’t working. Info The varchar data type (AWS | Azure | GCP) is available in Databricks Runtime 8.0 and above. Create a simple Delta table, with one column as type string.%sql CREATE OR REPLACE TABLE delta_table1 (`col1` string) USING DELTA; Use SHOW TABLE on the newly created ta...

Vaccuming with zero retention results in data loss

Problem You add data to a Delta table, but the data disappears without warning. There is no obvious error message. Cause This can happen when spark.databricks.delta.retentionDurationCheck.enabled is set to false and VACUUM is configured to retain 0 hours. %sql VACUUM <name-of-delta-table> RETAIN 0 HOURS When VACUUM is configured to retain 0 ho...

Z-Ordering will be ineffective, not collecting stats

Problem You are trying to optimize a Delta table by Z-Ordering and receive an error about not collecting stats for the columns. AnalysisException: Z-Ordering on [col1, col2] will be ineffective, because we currently do not collect stats for these columns. Info Please review Z-Ordering (multi-dimensional clustering) (AWS | Azure | GCP) for more infor...

Change cluster config for Delta Live Table pipeline

Problem You are using Delta Live Tables and want to change the cluster configuration. You create a pipeline, but only have options to enable or disable Photon and select the number of workers. Cause When you create a Delta Live Table pipeline, most parameters are configured with default values. These values cannot be configured before the pipeline i...

Distinguish active and dead jobs

Problem On clusters where there are too many concurrent jobs, you often see some jobs stuck in the Spark UI without any progress. This complicates identifying which are the active jobs/stages versus the dead jobs/stages. Cause Whenever there are too many concurrent jobs running on a cluster, there is a chance that the Spark internal eventListenerBus...

Spark job fails with Driver is temporarily unavailable

Problem A Databricks notebook returns the following error: Driver is temporarily unavailable This issue can be intermittent or not. A related error message is: Lost connection to cluster. The notebook may have been detached. Cause One common cause for this error is that the driver is undergoing a memory bottleneck. When this happens, the driver cras...

How to delete all jobs using the REST API

Run the following commands to delete all jobs in a Databricks workspace. Identify the jobs to delete and list them in a text file:%sh curl -X GET -u "Bearer: <token>" https://<databricks-instance>/api/2.0/jobs/list | grep -o -P 'job_id.{0,6}' | awk -F':' '{print $2}' >> job_id.txt Run the curlcommand in a loop to delete the identif...

Job cluster limits on notebook output

Problem You are running a notebook on a job cluster and you get an error message indicating that the output is too large. The output of the notebook is too large. Cause: rpc response (of 20975548 bytes) exceeds limit of 20971520 bytes Cause This error message can occur in a job cluster whenever the notebook output is greater then 20 MB. If you are u...

Job fails, but Apache Spark tasks finish

Problem Your Databricks job reports a failed status, but all Spark jobs and tasks have successfully completed. Cause You have explicitly called spark.stop() or System.exit(0) in your code. If either of these are called, the Spark context is stopped, but the graceful shutdown and handshake with the Databricks job service does not happen. Solution Do ...

Job fails due to job rate limit

Problem A Databricks notebook or Jobs API request returns the following error: Error : {"error_code":"INVALID_STATE","message":"There were already 1000 jobs created in past 3600 seconds, exceeding rate limit: 1000 job creations per 3600 seconds."} Cause This error occurs because the number of jobs per hour exceeds the limit of 1000 established by Da...

Job fails with invalid access token

Problem Long running jobs, such as streaming jobs, fail after 48 hours when using dbutils.secrets.get() (AWS | Azure | GCP). For example: %python streamingInputDF1 = (      spark     .readStream                            .format("delta")                    .table("default.delta_sorce")   ) def writeIntodelta(batchDF, batchId):   table_name = dbutil...

Task deserialization time is high

Problem Your tasks are running slower than expected. You review the stage details in the Spark UI on your cluster and see that task deserialization time is high. Cause Cluster-installed libraries (AWS | Azure | GCP) are only installed on the driver when the cluster is started. These libraries are only installed on the executors when the first tasks ...

Cannot import TabularPrediction from AutoGluon

Problem You are trying to import TabularPrediction from AutoGluon, but are getting an error message. ImportError: cannot import name 'TabularPrediction' from 'autogluon' (unknown location) This happens when AutoGluon is installed via a notebook or as a cluster-installed library (AWS | Azure | GCP). You can reproduce the error by running the import c...

How to correctly update a Maven library in Databricks

Problem You make a minor update to a library in the repository, but you don’t want to change the version number because it is a small change for testing purposes. When you attach the library to your cluster again, your code changes are not included in the library. Cause One strength of Databricks is the ability to install third-party or custom libra...

Init script fails to download Maven JAR

Problem You have an init script that is attempting to install a library via Maven, but it fails when trying to download a JAR. https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/0.4.1/rapids-4-spark_2.12-0.4.1.jar%0D Resolving repo1.maven.org (repo1.maven.org)... 151.101.248.209 Connecting to repo1.maven.org (repo1.maven.org)|151.101.248....

Install package using previous CRAN snapshot

Problem You are trying to install a library package via CRAN, and are getting a Library installation failed for library due to infra fault error message. Library installation failed for library due to infra fault for Some(cran { package: "<name-of-package>" } ). Error messages: java.lang.RuntimeException: Installation failed with message: Erro...

Install PyGraphViz

PyGraphViz Python libraries are used to plot causal inference networks. If you try to install PyGraphViz as a standard library, it fails due to dependency errors. PyGraphViz has the following dependencies: python3-dev graphviz libgraphviz-dev pkg-config Install via notebook Install the dependencies with apt-get.%sh sudo apt-get install -y python3-de...

Install Turbodbc via init script

Turbodbc is a Python module that uses the ODBC interface to access relational databases. It has dependencies on libboost-all-dev, unixodbc-dev, and python-dev packages, which need to be installed in order. You can install these manually, or you can use an init script to automate the install. Create the init script Run this sample script in a noteboo...

Cannot uninstall library from UI

Problem Usually, libraries can be uninstalled in the Clusters UI. If the checkbox to select the library is disabled, then it’s not possible to uninstall the library from the UI. Cause If you create a library using REST API version 1.2 and if auto-attach is enabled, the library is installed on all clusters. In this scenario, the Clusters UI checkbox ...

Error when installing Cartopy on a cluster

Problem You are trying to install Cartopy on a cluster and you receive a ManagedLibraryInstallFailed error message. java.lang.RuntimeException: ManagedLibraryInstallFailed: org.apache.spark.SparkException: Process List(/databricks/python/bin/pip, install, cartopy==0.17.0, --disable-pip-version-check) exited with code 1.   ERROR: Command errored out ...

Error when installing pyodbc on a cluster

Problem One of the following errors occurs when you use pip to install the pyodbc library. java.lang.RuntimeException: Installation failed with message: Collecting pyodbc "Library installation is failing due to missing dependencies. sasl and thrift_sasl are optional dependencies for SASL or Kerberos support" Cause Although sasl and thrift_sasl are o...

Libraries fail with dependency exception

Problem You have a Python function that is defined in a custom egg or wheel file and also has dependencies that are satisfied by another customer package installed on the cluster. When you call this function, it returns an error that says the requirement cannot be satisfied. org.apache.spark.SparkException: Process List(/local_disk0/pythonVirtualEnv...

Reading .xlsx files with xlrd fails

Problem You are have xlrd installed on your cluster and are attempting to read files in the Excel .xlsx format when you get an error. XLRDError: Excel xlsx file; not supported Cause xlrd 2.0.0 and above can only read .xls files. Support for .xlsx files was removed from xlrd due to a potential security vulnerability. Solution Use openpyxl to open .xl...

Remove Log4j 1.x JMSAppender and SocketServer classes from classpath

Databricks recently published a blog on Log4j 2 Vulnerability (CVE-2021-44228) Research and Assessment. Databricks does not directly use a version of Log4j known to be affected by this vulnerability within the Databricks platform in a way we understand may be vulnerable. Databricks also does not use the affected classes from Log4j 1.x with known vul...

Replace a default library jar

Databricks includes a number of default Java and Scala libraries. You can replace any of these libraries with another version by using a cluster-scoped init script to remove the default library jar and then install the version you require. Warning Removing default libraries and installing new versions may cause instability or completely break your D...

Python command fails with AssertionError: wrong color format

Problem You run a Python notebook and it fails with an AssertionError: wrong color format message. An example stack trace:   File "/local_disk0/tmp/1599775649524-0/PythonShell.py", line 39, in <module>     from IPython.nbconvert.filters.ansi import ansi2html   File "<frozen importlib._bootstrap>", line 983, in _find_and_load   File "<...

PyPMML fails with Could not find py4j jar error

Problem PyPMML is a Python PMML scoring library. After installing PyPMML in a Databricks cluster, it fails with a Py4JError: Could not find py4j jar error. %python from pypmml import Model modelb = Model.fromFile('/dbfs/shyam/DecisionTreeIris.pmml') Error : Py4JError: Could not find py4j jar at Cause This error occurs due to a dependency on the defa...

TensorFlow fails to import

Problem You have TensorFlow installed on your cluster. When you try to import TensorFlow, it fails with an Invalid Syntax or import error. Cause The version of protobuf installed on your cluster is not compatible with your version of TensorFlow. Solution Use a cluster-scoped init script to install TensorFlow with matching versions of NumPy and proto...

Verify the version of Log4j on your cluster

Databricks recently published a blog on Log4j 2 Vulnerability (CVE-2021-44228) Research and Assessment. Databricks does not directly use a version of Log4j known to be affected by this vulnerability within the Databricks platform in a way we understand may be vulnerable. If you are using Log4j within your cluster (for example, if you are processing ...

Apache Spark jobs fail with Environment directory not found error

Problem After you install a Python library (via the cluster UI or by using pip), your Apache Spark jobs fail with an Environment directory not found error message. org.apache.spark.SparkException: Environment directory not found at /local_disk0/.ephemeral_nfs/cluster_libraries/python Cause Libraries are installed on a Network File System (NFS) on th...

Conda fails to download packages from Anaconda

Problem You are attempting to download packages from the Anaconda repository and get a PackagesNotFoundError error message. This error can occur when using %conda, or %sh conda in notebooks, and when using Conda in an init script. Cause Anaconda Inc. updated the terms of service for repo.anaconda.com and anaconda.org/anaconda. Based on the Anaconda ...

Download artifacts from MLflow

By default, the MLflow client saves artifacts to an artifact store URI during an experiment. The artifact store URI is similar to /dbfs/databricks/mlflow-tracking/<experiment-id>/<run-id>/artifacts/. This artifact store is a MLflow managed location, so you cannot download artifacts directly. You must use client.download_artifacts in the ...

How to extract feature information for tree-based Apache SparkML pipeline models

When you are fitting a tree-based model, such as a decision tree, random forest, or gradient boosted tree, it is helpful to be able to review the feature importance levels along with the feature names. Typically models in SparkML are fit as the last stage of the pipeline. To extract the relevant feature information from the pipeline with the tree mo...

Fitting an Apache SparkML model throws error

Problem Databricks throws an error when fitting a SparkML model or Pipeline: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 162.0 failed 4 times, most recent failure: Lost task 0.3 in stage 162.0 (TID 168, 10.205.250.130, executor 1): org.apache.spark.SparkException: Failed to execute user defined function($anonfu...

H2O.ai Sparkling Water cluster not reachable

Problem You are trying to initialize H2O.ai’s Sparkling Water on Databricks Runtime 7.0 and above when you get a H2OClusterNotReachableException error message. %python import ai.h2o.sparkling._ val h2oContext = H2OContext.getOrCreate() ai.h2o.sparkling.backend.exceptions.H2OClusterNotReachableException: H2O cluster X.X.X.X:54321 - sparkling-water-ro...

How to perform group K-fold cross validation with Apache Spark

Cross validation randomly splits the training data into a specified number of folds. To prevent data leakage where the same data shows up in multiple folds you can use groups. scikit-learn supports group K-fold cross validation to ensure that the folds are distinct and non-overlapping. On Spark you can use the spark-sklearn library, which distribute...

MLflow project fails to access an Apache Hive table

Problem You have an MLflow project that fails to access a Hive table and returns a Table or view not found error. pyspark.sql.utils.AnalysisException: "Table or view not found: `default`.`tab1`; line 1 pos 21;\n'Aggregate [unresolvedalias(count(1), None)]\n+- 'UnresolvedRelation `default`.`tab1`\n" xxxxx ERROR mlflow.cli: === Run (ID 'xxxxx') failed...

How to speed up cross-validation

Hyperparameter tuning of Apache SparkML models takes a very long time, depending on the size of the parameter grid. You can improve the performance of the cross-validation step in SparkML to speed things up: Cache the data before running any feature transformations or modeling steps, including cross-validation. Processes that refer to the data multi...

Hyperopt fails with maxNumConcurrentTasks error

Problem You are tuning machine learning parameters using Hyperopt when your job fails with a py4j.Py4JException: Method maxNumConcurrentTasks([]) does not exist error. You are using a Databricks Runtime for Machine Learning (Databricks Runtime ML) cluster. Cause Databricks Runtime ML has a compatible version of Hyperopt pre-installed (AWS | Azure | ...

Incorrect results when using documents as inputs

Problem You have a ML model that takes documents as inputs, specifically, an array of strings. You use a feature extractor like TfidfVectorizer to convert the documents to an array of strings and ingest the array into the model. The model is trained, and predictions happen in the notebook, but model serving doesn’t return the expected results for JS...

Experiment warning when custom artifact storage location is used

Problem When you create an MLflow experiment with a custom artifact location, you get the following warning: Cause MLflow experiment permissions (AWS | Azure | GCP) are enforced on artifacts in MLflow Tracking, enabling you to easily control access to datasets, models, and other files. MLflow cannot guarantee the enforcement of access controls on ar...

Experiment warning when legacy artifact storage location is used

Problem A new icon appears on the MLflow Experiments page with the following open access warning: Cause MLflow experiment permissions (AWS | Azure | GCP) are enforced on artifacts in MLflow Tracking, enabling you to easily control access to datasets, models, and other files. In MLflow 1.11 and above, new experiments store artifacts in an MLflow-mana...

KNN model using pyfunc returns ModuleNotFoundError or FileNotFoundError

Problem You have created a Sklearn model using KNeighborsClassifier and are using pyfunc to run a prediction. For example: %python import mlflow.pyfunc pyfunc_udf = mlflow.pyfunc.spark_udf(spark, model_uri=model_uri, result_type='string') predicted_df = merge.withColumn("prediction", pyfunc_udf(*merge.columns[1:])) predicted_df.collect() The predict...

OSError when accessing MLflow experiment artifacts

Problem You get an OSError: No such file or directory error message when trying to download or log artifacts using one of the following: MlflowClient.download_artifacts() mlflow.[flavor].log_model() mlflow.[flavor].load_model() mlflow.log_artifacts() OSError: No such file or directory: '/dbfs/databricks/mlflow-tracking/<experiment-id>/<run-...

PERMISSION_DENIED error when accessing MLflow experiment artifact

Problem You get a PERMISSION_DENIED error when trying to access an MLflow artifact using the MLflow client. RestException: PERMISSION_DENIED: User <user> does not have permission to 'View' experiment with id <experiment-id> or RestException: PERMISSION_DENIED: User <user> does not have permission to 'Edit' experiment with id <ex...

Runs are not nested when SparkTrials is enabled in Hyperopt

Problem SparkTrials is an extension of Hyperopt, which allows runs to be distributed to Spark workers. When you start an MLflow run with nested=True in the worker function, the results are supposed to be nested under the parent run. Sometimes the results are not correctly nested under the parent run, even though you ran SparkTrials with nested=True ...

Autoscaling is slow with an external metastore

Problem You have an external metastore configured on your cluster and autoscaling is enabled, but the cluster is not autoscaling effectively. Cause You are copying the metastore jars to every executor, when they are only needed in the driver. It takes time to initialize and run the jars every time a new executor spins up. As a result, adding more ex...

Data too long for column error

Problem You are trying to insert a struct into a table, but you get a java.sql.SQLException: Data too long for column error. Caused by: java.sql.SQLException: Data too long for column 'TYPE_NAME' at row 1 Query is: INSERT INTO COLUMNS_V2 (CD_ID,COMMENT,`COLUMN_NAME`,TYPE_NAME,INTEGER_IDX) VALUES (?,?,?,?,?) , parameters [103182,<null>,'address...

Drop database without deletion

By default, the DROP DATABASE (AWS | Azure | GCP) command drops the database and deletes the directory associated with the database from the file system. Sometimes you may want to drop the database, but keep the underlying database directory intact. Example code You can use this example code to drop the database without dropping the underlying stora...

Error in CREATE TABLE with external Hive metastore

Problem You are connecting to an external MySQL metastore and attempting to create a table when you get an error. AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:An exception was thrown while adding/validating class(es) : (conn=21) Column length too big for column 'PARAM_VALUE' (max = 16383); use BLOB or TE...

Japanese character support in external metastore

Problem You are trying to use Japanese characters in your tables, but keep getting errors. Create a table with the OPTIONS keyword OPTIONS provides extra metadata to the table. You try creating a table with OPTIONS and specify the charset as utf8mb4. %sql CREATE TABLE default.JPN_COLUMN_NAMES('作成年月' string ,'計上年月' string ,'所属コード' string ,'生保代理店コード＿８...

Parquet timestamp requires Hive metastore 1.2 or above

Problem You are trying to create a Parquet table using TIMESTAMP, but you get an error message. Error in SQL statement: QueryExecutionException: FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. java.lang.UnsupportedOperationException: Parquet does not support timestamp. See HIVE-6384 Example code %sql CREATE EXTERN...

JSON reader parses values as null

Problem You are attempting to read a JSON file. You know the file has data in it, but the Apache Spark JSON reader is returning a null value. Example code You can use this example code to reproduce the problem. Create a test JSON file in DBFS.%python dbutils.fs.rm("dbfs:/tmp/json/parse_test.txt") dbutils.fs.put("dbfs:/tmp/json/parse_test.txt", """ {...

display() does not show microseconds correctly

Problem You want to display a timestamp value with microsecond precision, but when you use display() it does not show the value past milliseconds. For example, this Apache Spark SQL display() command: %sql display(spark.sql("select cast('2021-08-10T09:08:56.740436' as timestamp) as test")) Returns a truncated value: 2021-08-10T09:08:56.740+0000 Caus...

Error: Received command c on object id p0

Problem You have imported Python libraries, but when you try to execute Python code in a notebook you get a repeating message as output. INFO:py4j.java_gateway:Received command c on object id p0 INFO:py4j.java_gateway:Received command c on object id p0 INFO:py4j.java_gateway:Received command c on object id p0 INFO:py4j.java_gateway:Received command ...

Failure when accessing or mounting storage

Problem You are trying to access an existing mount point, or create a new mount point, and it fails with an error message. Invalid Mount Exception:The backend could not get tokens for path /mnt. Cause The root mount path (/mnt) is also mounted to a storage location. You can verify that something is mounted to the root path by listing all mount point...

Item was too large to export

Problem You are trying to export notebooks using the workspace UI and are getting an error message. This item was too large to export. Try exporting smaller or fewer items. Cause The notebook files are larger than 10 MB in size. Solution The simplest solution is to limit the size of the notebook or folder that you are trying to download to 10 MB or ...

Update job permissions for multiple users

When you are running jobs, you might want to update user permissions for multiple users. You can do this by using the Databricks job permissions API (AWS | Azure | GCP) and a bit of Python code. Instructions Copy the example code into a notebook. Enter the <job-id> (or multiple job ids) into the array arr[]. Enter your payload{}. In this examp...

Generate browser HAR files

When troubleshooting UI issues, it is sometimes necessary to obtain additional information about the network requests that are generated in your browser. If this is needed, our support team will ask you to generate a HAR file. This article describes how to generate a HAR file with each of the major web browsers. Warning HAR files contain sensitive d...

Append output is not supported without a watermark

Problem You are performing an aggregation using append mode and an exception error message is returned. Append output mode not supported when there are streaming aggregations on streaming DataFrames/DataSets without watermark Cause You cannot use append mode on an aggregated DataFrame without a watermark. This is by design. Solution You must apply a...

Apache Spark DStream is not supported

Problem You are attempting to use a Spark Discretized Stream (DStream) in a Databricks streaming job, but the job is failing. Cause DStreams and the DStream API are not supported by Databricks. Solution Instead of using Spark DStream, you should migrate to Structured Streaming. Review the Databricks Structured Streaming in production (AWS | Azure | ...

Streaming with File Sink: Problems with recovery if you change checkpoint or output directories

When you stream data into a file sink, you should always change both checkpoint and output directories together. Otherwise, you can get failures or unexpected outputs. Apache Spark creates a folder inside the output directory named _spark_metadata. This folder contains write-ahead logs for every batch run. This is how Spark gets exactly-once guarant...

Get the path of files consumed by Auto Loader

When you process streaming files with Auto Loader (AWS | Azure | GCP), events are logged based on the files created in the underlying storage. This article shows you how to add the file path for every filename to a new column in the output DataFrame. One use case for this is auditing. When files are ingested to a partitioned folder structure there i...

How to restart a structured streaming query from last written offset

Scenario You have a stream, running a windowed aggregation query, that reads from Apache Kafka and writes files in Append mode. You want to upgrade the application and restart the query with the offset equal to the last written offset. You want to discard all state information that hasn’t been written to the sink, start processing from the earliest ...

Kafka error: No resolvable bootstrap urls

Problem You are trying to read or write data to a Kafka stream when you get an error message. kafkashaded.org.apache.kafka.common.KafkaException: Failed to construct kafka consumer Caused by: kafkashaded.org.apache.kafka.common.config.ConfigException: No resolvable bootstrap urls given in bootstrap.servers If you are running a notebook, the error me...

readStream() is not whitelisted error when running a query

Problem You have table access control (AWS | Azure | GCP) enabled on your cluster. You are trying to run a structured streaming query and get and error message. py4j.security.Py4JSecurityException: Method public org.apache.spark.sql.streaming.DataStreamReader org.apache.spark.sql.SQLContext.readStream() is not whitelisted on class class org.apache.s...

Checkpoint files not being deleted when using display()

Problem You have a streaming job using display() to display DataFrames. %scala val streamingDF = spark.readStream.schema(schema).parquet(<input_path>) display(streamingDF) Checkpoint files are being created, but are not being deleted. You can verify the problem by navigating to the root directory and looking in the /local_disk0/tmp/ folder. Ch...

Checkpoint files not being deleted when using foreachBatch()

Problem You have a streaming job using foreachBatch() to process DataFrames. %scala streamingDF.writeStream.outputMode("append").foreachBatch { (batchDF: DataFrame, batchId: Long) =>   batchDF.write.format("parquet").mode("overwrite").save(output_directory) }.start() Checkpoint files are being created, but are not being deleted. You can verify th...

Conflicting directory structures error

Problem You have an Apache Spark job that is failing with a Java assertion error java.lang.AssertionError: assertion failed: Conflicting directory structures detected. Example stack trace Caused by: org.apache.spark.sql.streaming.StreamingQueryException: There was an error when trying to infer the partition schema of the current batch of files. Plea...

RocksDB fails to acquire a lock

Problem You are trying to use RocksDB as a state store for your structured streaming application, when you get an error message saying that the instance could not be acquired. Caused by: java.lang.IllegalStateException: RocksDB instance could not be acquired by [ThreadId: 742, task: 140.3 in stage 3152, TID 553193] as it was not released by [ThreadI...

Streaming job gets stuck writing to checkpoint

Problem You are monitoring a streaming job, and notice that it appears to get stuck when processing data. When you review the logs, you discover the job gets stuck when writing data to a checkpoint. INFO HDFSBackedStateStoreProvider: Deleted files older than 381160 for HDFSStateStoreProvider[id = (op=0,part=89),dir = dbfs:/FileStore/R_CHECKPOINT5/st...

AttributeError: ‘function’ object has no attribute

Problem You are selecting columns from a DataFrame and you get an error message. ERROR: AttributeError: 'function' object has no attribute '_get_object_id' in job Cause The DataFrame API contains a small number of protected keywords. If a column in your DataFrame uses a protected keyword as the column name, you will get an error message. For example...

Convert Python datetime object to string

There are multiple ways to display date and time values with Python, however not all of them are easy to read. For example, when you collect a timestamp column from a DataFrame and save it as a Python variable, the value is stored as a datetime object. If you are not familiar with the datetime object format, it is not as easy to read as the common Y...

Display file and directory timestamp details

In this article we show you how to display detailed timestamps, including the date and time when a file was created or modified. Use ls command The simplest way to display file timestamps is to use the ls -lt <path> command in a bash shell. For example, this sample command displays basic timestamps for files and directories in the /dbfs/ folde...

Reading large DBFS-mounted files using Python APIs

This article explains how to resolve an error that occurs when you read large DBFS-mounted files using local Python APIs. Problem If you mount a folder onto dbfs:// and read a file larger than 2GB in a Python API like pandas, you will see following error: /databricks/python/local/lib/python2.7/site-packages/pandas/parser.so in pandas.parser.TextRead...

How to import a custom CA certificate

When working with Python, you may want to import a custom CA certificate to avoid connection errors to your endpoints. ConnectionError: HTTPSConnectionPool(host='my_server_endpoint', port=443): Max retries exceeded with url: /endpoint (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7fb73dc3b3d0>: Failed t...

Job remains idle before starting

Problem You have an Apache Spark job that is triggered correctly, but remains idle for a long time before starting. You have a Spark job that ran well for awhile, but goes idle for a long time before resuming. Symptoms include: Cluster downscales to the minimum number of worker nodes during idle time. Driver logs don’t show any Spark jobs during idl...

List all workspace objects

You can use the Databricks Workspace API (AWS | Azure | GCP) to recursively list all workspace objects under a given path. Common use cases for this include: Indexing all notebook names and types for all users in your workspace. Use the output, in conjunction with other API calls, to delete unused workspaces or to manage notebooks. Dynamically get t...

Python commands fail on high concurrency clusters

Problem You are attempting to run Python commands on a high concurrency cluster. All Python commands fail with a WARN error message. WARN PythonDriverWrapper: Failed to start repl ReplId-61bef-9fc33-1f8f6-2 ExitCodeException exitCode=1: chown: invalid user: ‘spark-9fcdf4d2-045d-4f3b-9293-0f’ Cause Both spark.databricks.pyspark.enableProcessIsolation...

Cluster cancels Python command execution after installing Bokeh

Problem The cluster returns Cancelled in a Python notebook. Inspect the driver log (std.err) in the Cluster Configuration page for a stack trace and error message similar to the following: log4j:WARN No appenders could be found for logger (com.databricks.conf.trusted.ProjectConf$). log4j:WARN Please initialize the log4j system properly. log4j:WARN S...

Cluster cancels Python command execution due to library conflict

Problem The cluster returns Cancelled in a Python notebook. Notebooks in all other languages execute successfully on the same cluster. Cause When you install a conflicting version of a library, such as ipython, ipywidgets, numpy, scipy, or pandas to the PYTHONPATH, then the Python REPL can break, causing all commands to return Cancelled after 30 sec...

Python command execution fails with AttributeError

This article can help you resolve scenarios in which Python command execution fails with an AttributeError. Problem: 'tuple' object has no attribute 'type' When you run a notebook, Python command execution fails with the following error and stack trace: AttributeError: 'tuple' object has no attribute 'type' Traceback (most recent call last): File "/...

Change version of R (r-base)

These instructions describe how to install a different version of R (r-base) on a cluster. You can check the default r-base version that each Databricks Runtime version is installed with in the System environment section of each Databricks Runtime release note (AWS | Azure | GCP). List available r-base-core versions To list the versions of r-base-co...

Fix the version of R packages

When you use the install.packages() function to install CRAN packages, you cannot specify the version of the package, because the expectation is that you will install the latest version of the package and it should be compatible with the latest version of its dependencies. If you have an outdated dependency installed, it will be updated as well. Som...

How to parallelize R code with gapply

Parallelization of R code is difficult, because R code runs on the driver and R data.frames are not distributed. Often, there is existing R code that is run locally and that is converted to run on Apache Spark. In other cases, some SparkR functions used for advanced statistical analysis and machine learning techniques may not support distributed com...

How to parallelize R code with spark.lapply

Parallelization of R code is difficult, because R code runs on the driver and R data.frames are not distributed. Often, there is existing R code that is run locally and that is converted to run on Apache Spark. In other cases, some SparkR functions used for advanced statistical analysis and machine learning techniques may not support distributed com...

Install rJava and RJDBC libraries

This article explains how to install rJava and RJBDC libraries. Problem When you install rJava and RJDBC libraries with the following command in a notebook cell: %r install.packages(c("rJava", "RJDBC")) You observe the following error: ERROR: configuration failed for package 'rJava' Cause The rJava and RJDBC packages check for Java dependencies and ...

Rendering an R markdown file containing sparklyr code fails

Problem After you install and configure RStudio in the Databricks environment, when you launch RStudio and click the Knit button to knit a Markdown file that contains code to initialize a sparklyr context, rendering fails with the following error: failed to start sparklyr backend:object 'DATABRICKS_GUID' not found Calls: <Anonymous>… tryCatch ...

Resolving package or namespace loading error

This article explains how to resolve a package or namespace loading error. Problem When you install and load some libraries in a notebook cell, like: %r library(BreakoutDetection) You may get a package or namespace error: Loading required package: BreakoutDetection: Error : package or namespace load failed for ‘BreakoutDetection’ in loadNamespace(i,...

RStudio server backend connection error

Problem You get a backend connection error when using RStudio server. Error in Sys.setenv(EXISTING_SPARKR_BACKEND_PORT = system(paste0("wget -qO - 'http://localhost:6061/?type=\"com.databricks.backend.common.rpc.DriverMessages$StartRStudioSparkRBackend\"' --post-data='{\"@class\":\"com.databricks.backend.common.rpc.DriverMessages$StartRStudioSparkRB...

Verify R packages installed via init script

When you configure R packages to install via an init script, it is possible for a package install to fail if dependencies are not installed. You can use the R commands in a notebook to check that all of the packages correctly installed. Info This article does require you to provide a list of packages to check against. List installed packages Make a ...

Apache Spark UI is not in sync with job

Problem The status of your Spark jobs is not correctly shown in the Spark UI (AWS | Azure | GCP). Some of the jobs that are confirmed to be in the Completed state are shown as Active/Running in the Spark UI. In some cases the Spark UI may appear blank. When you review the driver logs, you see an AsyncEventQueue warning. Logs ===== 20/12/23 21:20:26 ...

Apache Spark job fails with Parquet column cannot be converted error

Problem You are reading data in Parquet format and writing to a Delta table when you get a Parquet column cannot be converted error message. The cluster is running Databricks Runtime 7.3 LTS or above. org.apache.spark.SparkException: Task failed while writing rows. Caused by: com.databricks.sql.io.FileReadException: Error while reading file s3://buc...

Best practice for cache(), count(), and take()

cache() is an Apache Spark transformation that can be used on a DataFrame, Dataset, or RDD when you want to perform more than one action. cache() caches the specified DataFrame, Dataset, or RDD in the memory of your cluster’s workers. Since cache() is a transformation, the caching operation takes place only when a Spark action (for example, count(),...

Cannot import timestamp_millis or unix_millis

Problem You are trying to import timestamp_millis or unix_millis into a Scala notebook, but get an error message. %scala import org.apache.spark.sql.functions.{timestamp_millis, unix_millis} error: value timestamp_millis is not a member of object org.apache.spark.sql.functions import org.apache.spark.sql.functions.{timestamp_millis, unix_millis} Cau...

Cannot modify the value of an Apache Spark config

Problem You are trying to SET the value of a Spark config in a notebook and get a Cannot modify the value of a Spark config error. For example: %sql SET spark.serializer=org.apache.spark.serializer.KryoSerializer Error in SQL statement: AnalysisException: Cannot modify the value of a Spark config: spark.serializer; Cause The SET command does not wor...

Convert nested JSON to a flattened DataFrame

This article shows you how to flatten nested JSON, using only $"column.*" and explode methods. Sample JSON file Pass the sample JSON string to the reader. %scala val json =""" {         "id": "0001",         "type": "donut",         "name": "Cake",         "ppu": 0.55,         "batters":                 {                         "batter":           ...

Create a DataFrame from a JSON string or Python dictionary

In this article we are going to review how you can create an Apache Spark DataFrame from a variable containing a JSON string or a Python dictionary. Create a Spark DataFrame from a JSON string Add the JSON content from the variable to a list.%scala import scala.collection.mutable.ListBuffer val json_content1 = "{'json_col1': 'hello', 'json_col2': 32...

Decimal$DecimalIsFractional assertion error

Problem You are running a job on Databricks Runtime 7.x or above when you get a java.lang.AssertionError: assertion failed: Decimal$DecimalIsFractional error message. Example stack trace: java.lang.AssertionError: assertion failed:  Decimal$DecimalIsFractional   while compiling: <notebook>    during phase: globalPhase=terminal, enteringPhase=j...

from_json returns null in Apache Spark 3.0

Problem The from_json function is used to parse a JSON string and return a struct of values. For example, if you have the JSON string [{"id":"001","name":"peter"}], you can pass it to from_json with a schema and get parsed struct values in return. %python from pyspark.sql.functions import col, from_json display(   df.select(col('value'), from_json(c...

Manage the size of Delta tables

Delta tables are different than traditional tables. Delta tables include ACID transactions and time travel features, which means they maintain transaction logs and stale data files. These additional features require storage space. In this article we discuss recommendations that can help you manage the size of your Delta tables. Enable file system ve...

Select files using a pattern match

When selecting files, a common requirement is to only read specific files from a folder. For example, if you are processing logs, you may want to read files from a specific month. Instead of enumerating each file and folder to find the desired files, you can use a glob pattern to match multiple files with a single expression. This article uses examp...

Date functions only accept int values in Apache Spark 3.0

Problem You are attempting to use the date_add() or date_sub() functions in Spark 3.0, but they are returning an Error in SQL statement: AnalysisException error message. In Spark 2.4 and below, both functions work as normal. %sql select date_add(cast('1964-05-23' as date), '12.34') Cause You are attempting to use a fractional or string value as the ...

Duplicate columns in the metadata error

Problem Your Apache Spark job is processing a Delta table when the job fails with an error message. org.apache.spark.sql.AnalysisException: Found duplicate column(s) in the metadata update: col1, col2... Cause There are duplicate column names in the Delta table. Column names that differ only by case are considered duplicate. Delta Lake is case prese...

Generate unique increasing numeric values

This article shows you how to use Apache Spark functions to generate unique increasing numeric values in a column. We review three different methods to use. You should select the method that works best with your use case. Use zipWithIndex() in a Resilient Distributed Dataset (RDD) The zipWithIndex() function is only available within RDDs. You cannot...

Error in SQL statement: AnalysisException: Table or view not found

Problem When you try to query a table or view, you get this error: AnalysisException:Table or view not found when trying to query a global temp view Cause You typically create global temp views so they can be accessed from different sessions and kept alive until the application ends. You can create a global temp view with the following statement: %s...

Error when downloading full results after join

Problem You are working with two tables in a notebook. You perform a join. You can preview the output, but when you try to Download full results you get an error. Error in SQL statement: AnalysisException: Found duplicate column(s) when inserting into dbfs:/databricks-results/ Reproduce error Create two tables.%python from pyspark.sql.functions impo...

Error when running MSCK REPAIR TABLE in parallel

Problem You are trying to run MSCK REPAIR TABLE <table-name> commands for the same table in parallel and are getting java.net.SocketTimeoutException: Read timed out or out of memory error messages. Cause When you try to add a large number of new partitions to a table with MSCK REPAIR in parallel, the Hive metastore becomes a limiting factor, a...

Find the size of a table

This article explains how to find the size of a table. The command used depends on if you are trying to find the size of a delta table or a non-delta table. Size of a delta table To find the size of a delta table, you can use a Apache Spark SQL command. %scala import com.databricks.sql.transaction.tahoe._ val deltaLog = DeltaLog.forTable(spark, "dbf...

Inner join drops records in result

Problem You perform an inner join, but the resulting joined table is missing data. For example, assume you have two tables, orders and models. %python df_orders = spark.createDataFrame([('Nissan','Altima','2-door 2.5 S Coupe'), ('Nissan','Altima','4-door 3.5 SE Sedan'), ('Nissan','Altima',''), ('Nissan','Altima', None)], ["Company", "Model", "Info"]...

Data is incorrect when read from Snowflake

Problem You have a job that is using Apache Spark to read from a Snowflake table, but the time data that appears in the Dataframe is incorrect. If you run the same query directly on Snowflake, the correct time data is returned. Cause The time zone value was not correctly set. A mismatch between the time zone value of the Databricks cluster and Snowf...

JDBC write fails with a PrimaryKeyViolation error

Problem You are using JDBC to write to a SQL table that has primary key constraints, and the job fails with a PrimaryKeyViolation error. Alternatively, you are using JDBC to write to a SQL table that does not have primary key constraints, and you see duplicate entries in recently written tables. Cause When Apache Spark performs a JDBC write, one par...

Query does not skip header row on external table

Problem You are attempting to query an external Hive table, but it keeps failing to skip the header row, even though TBLPROPERTIES ('skip.header.line.count'='1') is set in the HiveContext. You can reproduce the issue by creating a table with this sample code. %sql CREATE EXTERNAL TABLE school_test_score (   `school` varchar(254),   `student_id` varc...

SHOW DATABASES command returns unexpected column name

Problem You are using the SHOW DATABASES command and it returns an unexpected column name. Cause The column name returned by the SHOW DATABASES command changed in Databricks Runtime 7.0. Databricks Runtime 6.4 Extended Support and below: SHOW DATABASES returns namespace as the column name. Databricks Runtime 7.0 and above: SHOW DATABASES returns dat...

Cannot view table SerDe properties

Problem You are trying to view the SerDe properties on an Apache Hive table, but SHOW CREATE TABLE just returns the Apache Spark DDL. It does not show the SerDe properties. For example, given this sample code: %sql SHOW CREATE TABLE <table-identifier> You get a result that does not show the SerDe properties: Cause You are using Databricks Runt...