Re: Java Jre 8 Standalone Download
Brie Hoffler
Delta Standalone minimizes memory usage in the JVM by loading the Delta Lake transaction log incrementally, using an iterator. However, Delta Standalone runs in a single JVM, and is limited to the processing and memory capabilities of that JVM. Users must configure the JVM to avoid out of memory (OOM) issues.
Delta Standalone does provide basic APIs for reading Parquet data, but does not include APIs for writing Parquet data. Users must write out new Parquet data files themselves and then use Delta Standalone to commit those changes to the Delta table and make the new data visible to readers.
Delta Standalone provides classes and entities to read data, query metadata, and commit to the transaction log. A few of them are highlighted here and with their key interfaces. See the Java API docs for the full set of classes and entities.
The main class for committing a set of updates to the transaction log is OptimisticTransaction. During a transaction, all reads must go through the OptimisticTransaction instance rather than the DeltaLog in order to detect logical conflicts and concurrent updates.
Get the latest version committed for a given application ID (for example, for idempotency) with OptimisticTransaction::txnVersion. (Note that this API requires users to commit SetTransaction actions.)
The only public APIs currently provided by Delta Standalone are in the io.delta.standalone package. Classes and methods in the io.delta.standalone.internal package are considered internal and are subject to change across minor and patch releases.
You can add the Delta Standalone library as a dependency by using your preferred build tool. Delta Standalone depends upon the hadoop-client and parquet-hadoop packages. Example build files are listed in the following sections.
Delta Standalone shades its own Parquet dependencies so that it works out-of-the-box and reduces dependency conflicts in your environment. However, if you would like to use utility class io.delta.standalone.util.ParquetSchemaConverter, then you must provide your own version of org.apache.parquet:parquet-hadoop.
Because storage systems do not necessarily provide all of these guarantees out-of-the-box, Delta Lake transactional operations typically go through the LogStore API instead of accessing the storage system directly. To provide the ACID guarantees for different storage systems, you may have to use different LogStore implementations. This section covers how to configure Delta Standalone for various storage systems. There are two categories of storage systems:
Storage systems with built-in support: For some storage systems, you do not need additional configurations. Delta Standalone uses the scheme of the path (that is, s3a in s3a://path) to dynamically identify the storage system and use the corresponding LogStore implementation that provides the transactional guarantees. However, for S3, there are additional caveats on concurrent writes. See the section on S3 for details.
Other storage systems: The LogStore, similar to Apache Spark, uses the Hadoop FileSystem API to perform reads and writes. Delta Standalone supports concurrent reads on any storage system that provides an implementation of the FileSystem API. For concurrent writes with transactional guarantees, there are two cases based on the guarantees provided by the FileSystem implementation. If the implementation provides consistent listing and atomic renames-without-overwrite (that is, rename(... , overwrite = false) will either generate the target file atomically or fail if it already exists with java.nio.file.FileAlreadyExistsException), then the default LogStore implementation using renames will allow concurrent writes with guarantees. Otherwise, you must configure a custom implementation of LogStore by setting the following Hadoop configuration when you instantiate a DeltaLog with DeltaLog.forTable(hadoopConf, path):
Here, is the scheme of the paths of your storage system. This configures Delta Standalone to dynamically use the given LogStore implementation only for those paths. You can have multiple such configurations for different schemes in your application, thus allowing it to simultaneously read and write from different storage systems.
Before version 0.5.0, Delta Standalone supported configuring LogStores by setting io.delta.standalone.LOG_STORE_CLASS_KEY. This approach is now deprecated. Setting this configuration will use the configured LogStore for all paths, thereby disabling the dynamic scheme-based delegation.
By default, Delta Standalone supports concurrent reads from multiple clusters. However, concurrent writes to S3 must originate from a single cluster to provide transactional guarantees. This is because S3 currently does not provide mutual exclusion, that is, there is no way to ensure that only one writer is able to create a file.
To use Delta Standalone with S3, you must meet the following requirements. If you are using access keys for authentication and authorization, you must configure a Hadoop Configuration specified as follows when you instantiate a DeltaLog with DeltaLog.forTable(hadoopConf, path).
This mode supports concurrent writes to S3 from multiple clusters. Enable multi-cluster support by configuring Delta Standalone to use the correct LogStore implementation. This implementation uses DynamoDB to provide mutual exclusion.
When writing from multiple clusters, all drivers must use this LogStore implementation and the same DynamoDB table and region. If some drivers use the default LogStore while others use this experimental LogStore then data loss can occur.
By this point, this multi-cluster setup is fully operational. However, there is extra configuration you may do to improve performance and optimize storage when running in production. See the Delta Lake documentation for more details.
Delta Standalone supports concurrent reads and writes from multiple clusters with full transactional guarantees for various Azure storage systems. To use an Azure storage system, you must satisfy the following requirements, and configure a Hadoop Configuration as specified when you instantiate a DeltaLog with DeltaLog.forTable(hadoopConf, path).
We have a Delta table Sales storing sales data, but have realized all the data written on November 2021 for customer XYZ had incorrect total_cost values. Thus, we need to update all those records with the correct values. We will use a fictious distributed engine Zappy and Delta Standalone to update our Delta table.
Since we must read existing data in order to perform the desired update operation, we must use OptimisticTransaction::markFilesAsRead in order to automatically detect any concurrent modifications made to our read partitions. Since Delta Standalone only supports partition pruning, we must apply the residual predicate to further filter the returned files.
For most use cases, and especially when you deal with large volumes of data, we recommend that you use the Delta Standalone library as your metadata-only reader, and then perform the Parquet data reading yourself, most likely in a distributed manner.
Delta Standalone provides two APIs for reading the files in a given table snapshot. Snapshot::getAllFiles returns an in-memory list. As of 0.3.0, we also provide Snapshot::scan(filter)::getFiles, which supports partition pruning and an optimized internal iterator implementation. We will use the latter here.
in jbpm4.4, I was able to put the required library jars, and jbpm jars and set up a database that confirms to jbpm DB requirements (I had to dissect the jbpm4.4 build xml file though) and I was able to run jbpm completely standalone.
jBPM5 has exactly same way of being integrated as jbpm4. You need to include it's libraries and dependencies into an application, then confiugure data source and persistence if you need it. All jbpm code bases tests run in standalone mode so you can take a look there as well.
if I connect on 9080, I see java.util.concurrent.ExecutionException: io.grpc.StatusRuntimeException: UNAVAILABLE: io exception. If I connect on 8080, I see java.util.concurrent.ExecutionException: io.grpc.StatusRuntimeException: INTERNAL: http2 exception
I have written standalone application program that perform some operations regarding customer. This application performance goes down when count of customers increases. I want to profile application perfomance to check which queries are taking time and total response time taken per customer.
maybe someone could help me out here on this matter. I have only had experience with the JAVA-based (I'd call it "classical") Processing. Nevertheless, I have now a support question from a colleague who uses Python Mode, which I have not been able to answer offhand. Thus, I would be more than thankful if someone here has a quick and helpful answer on this:
My colleague works with Python Mode Processing on Windows 10 and has been able to successfully write his first program. However, he has failed in creating a standalone application as it seems JAVA 7 is required, but he has JAVA 8 installed.
Processing.py specifically requires Java because it used Jython to run Python-on-JVM. Jython is why Processing Java libraries works with it -- and why it is restricted to pure (uncompiled) Python 2.7 only.
In AnyLogic Professional, you can export your models as standalone Java applications. This provides users with the ability to run AnyLogic models standalone without AnyLogic being installed on their machines.
A simulation application (the AnyLogic model exported as a standalone Java application) does not require AnyLogic installed on the computer and can be run on any Java-enabled machine (computer with Java Standard Edition 9.0 or installed). To download the latest version, go to the Open JDK website.
If the exported model contains data from the built-in database, make sure to allocate enough memory both for the model and the DB server in the Maximum available memory property of the specified experiment before you begin the export process.
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