Kyvos has introduced now comes with an in-house data storage layer called the Kyvos Analytical Store. The data store is designed to support complex, long-running queries that process significant portions of the stored dataset. This layer provides efficient data storage and retrieval capabilities that help to optimize both cost and time savings. The Kyvos Analytical Store is specifically designed to support complex, long-running queries that process substantial portions of the stored dataset. It is equipped with a columnar-vectorized query execution engine that processes a large batch of values, referred to as a "vector", in a single operation. This vectorized query execution significantly improves performance, especially in OLAP queries.
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Note When using semantic models with Kyvos Analytical Store, you must process the full data of the semantic model if it has not been processed before. Additionally, it is recommended to avoid using a previously processed semantic model after enabling the Kyvos Analytical Store. This is because executing queries on a previously processed semantic model may slow down as a result of Kyvos Analytical Store memory calculations. |
Certified Providers and supported Operating Systems for Kyvos Analytical Store
The Kyvos 2024.1.x is certified to work on the following provider and OS versions with the Kyvos Analytical Store.
Provider | OS Version |
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CDP 7.1.8/CDH-6.3.2 | Ubuntu 18.04 |
AWS | Centos 7.9 and Amazon Linux 2 |
Azure | RHEL 8.6 and RHEL7. |
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GCP | CentOS 7.9, RHEL 8.8, and Ubuntu 20.04 |
Prerequisites
To use Kyvos Analytical Store, you must ensure the following:
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Set the value of ENABLE_KYVOS_ANALYTICAL_STORE property
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as Yes.
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Set the value of ELASTIC_DEPLOYMENT
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as Disable.
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Set the value of kyvos.cuboid.replication.type property
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as Local on the semantic model.
Using Kyvos Analytical Store for semantic models
To use the Kyvos Analytical store as data storage layer for a semantic model, use the the following property on the semantic model.
Property: kyvos.build.aggregate.type
Value: Tabular
Scope: Semantic model advanced property
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To add Kyvos Analytical Store Jar in class pathclasspath, perform the following steps.
Login to Kyvos and navigate to the Connections page.
On the Connections page, click Properties.
On the Edit Properties dialog box, search “kyvos.connection.classpath.common.jar” and enter the ../lib/duckdb_jdbc.jar value in the existing class path.
Restart Kyvos services.
Using Kyvos Analytical Store for semantic models
To use the Kyvos Analytical store as data storage layer for a semantic model, use the following property on the semantic model.
Property: kyvos.build.aggregate.type
Value: Tabular
Scope: Semantic model advanced property
Kyvos Analytical Store memory calculations
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Parameter | Memory |
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Calculation | |
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HEAP_MEMORY | VM_TOTAL_MEMORY / 4 |
OFF_HEAP_MEMORY | VM_TOTAL_MEMORY / 8 |
KYVOS_ANALYTICAL_STORE_MEMORY | VM_TOTAL_MEMORY / 4 |
For example, If you have a 64 GB Query Engine Virtual Machine, the memory calculations are as follows:
HEAP_MEMORY = 16 GB
OFF_HEAP_MEMORY= 8 GB
KYVOS_ANALYTICAL_STORE_MEMORY= 16 GB
Memory tuning parameters
The Heap, Off-heap, and other required memory on Query Engines are automatically configured if memory mode is mode is set to Auto mode. For
For more details, see the Kyvos Java options page in Kyvos Manager.
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For memory configuration, perform the following post-deployment steps.
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Enhancements in Kyvos Analytical Store
Kyvos 2024.1.x
Kyvos comes with an enhanced Query Engine JVM configuration as per Kyvos Data Store Analytics configurations. To enable this feature, set the value of the ENABLE_KYVOS_ANALYTICAL_STORE property to 'Yes.' After configuring the Kyvos Data Store value, you must disable cluster scaling. Achieve this by setting the ELASTIC_DEPLOYMENT property to 'Disable.
Kyvos 2024.1
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Note
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Support for Parent-Child Hierarchy (PCH): With this release, Kyvos supports processing and browsing the semantic models having parent-child hierarchy, alternate hierarchy, and custom rollups in the Kyvos Analytical Store.
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Steps for Databricks
You must change Spark configurations to use managed disk. Ensure that you must not change the configuration in the default root (/tmp) volume.
To change Spark configurations to use managed disk, perform the following steps.
Databricks > Advance configuration > Spark
In the Spark Configurations, add the spark.local.dir /local_disk0 property where the local_disk0 is the managed disk.
This can be verified using the df -h command from a notebook
Add the SPARK_WORKER_DIR=/local_disk0 value in the Environment variables.