Guides → Configure Server
You can manage your server through the Cluster Management Console (CMC) by configuring various settings, such as an SQL interface port, connection pool size, proxy read timeout, and other settings. Here are the steps to configure your server:
- Sign in to the CMC.
- In the Navigation bar, select Clusters > Cluster-name.
- In the Canvas tabs, select Cluster Configurations > Server Configurations.
The following server configurations are available in the CMC:
Important
To avoid losing data after configuring your settings on a page, select Save before navigating to another page.
Clustering
The following table illustrates the cluster configuration property:
| Configuration Property | Control | Requires Restart | Description |
|---|---|---|---|
| Kafka Consumer Service Name | text box | Yes, Loader service only | Enter the name of the Loader service that acts as a Kafka consumer in the following format: <NODE_NAME>.<SERVICE_NAME> Note the following: ● If you do not configure the name of the Kafka Loader service, a random loader service will be automatically assigned leading to unexpected results. ● If you change the Kafka Loader service consumer from one Loader service (A) to another Loader service (B), you must restart the current Loader service (A) first, and then restart the Loader service (B). |
SQL Interface
Incorta can act as a PostgreSQL database using the Structured Query Language Interface (SQLi). Thus, you can utilize Incorta’s powerful engine performance and features through other BI tools, such as Tableau.
The following table illustrates the configuration properties of the SQL interface:
| Configuration Property | Control | Requires Restart | Description |
|---|---|---|---|
| SQL interface port | spin box | Yes, Analytics service only | Enter a port number to connect to the Incorta Engine. The Engine mode uses this port and runs the SQL interface queries against the data loaded in memory. |
| Data Store (DS) port | spin box | Yes, Analytics service only | Enter a port number to run queries directly using Spark. The Spark mode (Parquet mode) runs the SQL interface queries against the data loaded in the staging area using Incorta compacted parquet files. |
| New Metadata handler port | spin box | Yes, Analytics service only | Enter a port number for an auxiliary process to resolve metadata for SQLI clients. |
| Enable SSL for SQL interface ports | toggle | No | Enable this property to use Secure Sockets Layer (SSL) for SQLI connections. You must provide the SSL certificate details (path and passphrase). |
| SSL certificate(JKS) Path used for SQL interface | text box | No | Enter the path to SQLI SSL certificate. This property is only available when Enable SSL for SQL interface ports is toggled on. |
| SSL certificate(JKS) passphrase used for SQL interface | text box | No | Enter the passphrase to SQLI SSL certificate. This property is only available when Enable SSL for SQL interface ports is toggled on. |
| Enforce Accepting Only SSL Connections | toggle | No | Enable this property to allow SSL connections only. This property is only available when Enable SSL for SQL interface ports is toggled on. |
| Connection pool size | spin box | Yes, Analytics service only | Enter the number of pool connections. This creates multiple connections that improve the query response time. |
| External Tables CSV file path | text box | Yes, Analytics service only | Enter the CSV file path of your external tables. |
| Enable Cache | toggle | No | Enable this property to cache recurring SQL operations and enhance the performance of query execution, if there is an available cache size. |
| Cache size (In gigabytes) | spin box | No | Enter the maximum caching size (in GBs) of the data returned by the SQLi queries. When the cache size exceeds the limit, the least recently used (LRU) data is evicted to avail space for newer cache. You must set the cache size based on the available memory on the Incorta host server, and the size of the common query results. |
| Cached query result max size | spin box | No | Enter the maximum threshold size for each query result, which is the table cell count (rows multiplied by columns). If the size of a query result exceeds the threshold value, it will not be cached. |
| Enable cache auto refresh | toggle | No | Enable this property to automatically refresh the cache at specified intervals. |
| Refresh cache interval (In minutes) | spin box | No | Enter the interval in minutes to automatically refresh the cache. This property is only available when Enable cache auto refresh is toggled on. |
| Use column labels instead of column names | toggle | No | Enable this property to use table columns labels instead of column names during the SQL execution. This affects the column list specified in the query and the returned column names in the result. |
| SQL Engine Cache | toggle | No | Enable this property to use caching for SQL engine result set to speed up reptitive operations performed during the processing and the execution of a query. The default cache size is 5% of the off-heap memory. |
Spark Integration
Incorta utilizes Spark to execute complex queries that are not currently supported and to perform queries on data residing in the staging area without loading those data into Incorta’s memory.
The following table illustrates the Spark integration configuration properties:
| Configuration Property | Control | Requires Restart | Description |
|---|---|---|---|
| Spark master URL | text box | Yes, all services | Enter the Spark Master URL for the Apache Spark instance to execute materialized views (MVs) or SQL queries. Here are the steps to acquire the Spark Master information: ● Navigate to the Spark host server UI (from any browser), using the following format: <SPARK_HOST_SERVER>:<SPARK_PORT_NO> ● Copy the Spark Master connection string in the format: spark://<CONNECTION_STRING>:<SPARK_PORT_NO> ● The default port number for installed Spark on Incorta is 7077. |
| Enable a custom Delta Lake Reader | toggle | No | Enable this property to instruct Spark to use the Incorta Custom Delta Lake Reader to read data from the Delta Lake files instead of using Spark native Delta Lake reader . The Incorta Custom Delta Lake Reader is optimized for queries on relatively small data sets. Switching between these two readers may cause the first SQLi query on the Spark port to consume more time because the SQLi engine will be restarted. |
| Enable SQL App | toggle | No | Enable this property to start the SQL App, and then use Engine and Parquet modes to execute incoming SQL queries. |
| Enable spark fallback | toggle | Yes, Analytics service | This property is enabled by default. Disabling this feature prevents the In-Memory engine from falling back to the Spark or Parquet mode when the query is not executed, instead, an SQL error is returned. This property is only available when Enable SQL App is toggled on. |
| SQL App driver memory | spin box | No | Enter the app drive memory (in GBs) to be used by the SQL interface Spark. This memory is only used for constructing (not calculating) the final results. Consult with the Spark admin to set this value. |
| SQL App cores | spin box | No | Enter the number of dedicated Central Processing Unit (CPU) cores reserved for the SQL interface Spark application. Ensure that there are enough cores for the OS, other services, and applications. |
| SQL App memory | spin box | No | Enter the maximum memory (in GBs) used by SQL interface Spark queries. Note the following: ● You must set an adequate memory size when using materialized views. ● The memory required for both applications combined cannot exceed the Worker Memory. |
| SQL App executors | spin box | No | Enter the maximum number of executors that can be spawned on a single worker. Each of the executors will allocate some of the cores defined in sql.spark.application.cores, and will use part of the memory defined in sql.spark.application.memory . The cores and memory assigned per executor are equal. Thus, the number of SQL App cores and SQL App memory is divided by the number of executors. If the number of executors is greater than the SQL App memory, the executors will consume the assigned memory at 1 GB/executor. Examples: ● If the SQL App cores = 7 and the SQL App executors = 3, each executor will take 2 cores, and 1 of the cores will not be utilized. ● If the SQL App memory = 5 and the SQL App executors = 7, then 5 cores will be created, with 1 GB each. |
| SQL App shuffle partitions | spin box | No | Enter the number of SQL interface shuffle partitions. A single shuffle represents a block of data being processed to perform joins and/or aggregations. Note the following: ● The size of a shuffle partition increases as the size of processed data increases. ● The optimum shuffle partition size is around 128 MBs. ● Increase the shuffle partitions as the processed data size increases. However, an increase in shuffle partitions can lead to an increase in CPU utilization. ● If a query operates on a trivial amount of data, an increased amount of partitions will lead to a small partition size which may increase the query execution time due to the overhead of managing the unnecessary amount of partitions. Inadequate partition size may cause the query to fail. |
| SQL App extra options | text box | No | Enter the extra Spark options that you want to pass to the SQL interface Spark bridge application. These options can be used to override default configurations. Here are sample values: spark.sql.shuffle.partitions=8; spark.executor.memory=4g; spark.driver.memory=4g |
| Enable SQL App Dynamic Allocation | toggle | Yes, Analytics service | Enable this property to dynamically allocate the Data Hub Spark application by adjusting the number of application executors based on the workload. This property optimizes resource utilization as it removes idle executors, and claims them again if the workload increases. |
| Spark App port | spin box | Yes, Analytics service | Enter the port number to connect Incorta to Spark and access the Data Hub. |
| Spark App control channel port | spin box | No | Enter the control channel port number to send a shutdown signal to the Spark SQL app when the Incorta server requires Spark to shut down. |
| SQL App spark home (Optional) | text box | No | Optionally, enter the file system path for the Apache Spark instance that executes queries sent to the Spark App (Engine and Parquet modes). Note the following: ● The file system path is required for linking necessary libraries shipped by Apache Spark. ● Without a file system path, the " SPARK_HOME" environment variable is looked up instead. ● If the " SPARK_HOME" variable is not set, the value of the Spark instance for Incorta MVs and compaction is used instead. ● Else, an exception is thrown when the file system path is required to serve an SQL interface Spark (Engine or Parquet mode) query. |
| SQL App Spark master URL (Optional) | text box | No | Optionally, enter the URL of the Spark master server that executes the SQL interface queries over Incorta Engine or Parquet. The default URL is the Incorta-shipped Spark master URL. |
| Materialized view application cores | spin box | No | Enter the number of CPU cores for MVs. The default value is 1. The allocated cores for all running Spark applications cannot exceed the dedicated cores for the cluster unless Dynamic Allocation is enabled. Thus, the value will be used to compute the CPU cores for the initial executors. |
| Materialized view application memory | spin box | No | Enter the maximum memory size (in GBs) to use for MVs. The default is 1 GB. The memory for all Spark applications combined cannot exceed the cluster memory. |
| Materialized view application executors | spin box | No | Enter the maximum number of executors that can be spawned by a single materialized view application. Each of the executors will allocate some of the cores defined in sql.spark.mv.cores, and will consume part of the memory defined in sql.spark.mv.memory. The cores and memory assigned per executor are equal. For example, configuring an application with cores=4, memory=8, executors=2, will result in spawning 2 executors, in which each executor consumes 2cores/4GB from the cluster. |
| Materialized view driver memory | spin box | No | Enter the memory size (in GBs) to use in the MV driver process. The default value is 1 GB. |
| Enable dynamic allocation in MVs | toggle | No | Enable this property to dynamically allocate resources in MVs by scaling the number of application executors up and down based on the workload. This property requires running the Spark shuffle service. |
| Enable automatic ML library download | toggle | No | Enable this property to automatically download the Machine Learning (ML) library or use the bundled one. |
Tuning
The following table illustrates the tuning configuration properties:
| Configuration Property | Control | Requires Restart | Description |
|---|---|---|---|
| Enable parquet file Compression | toggle | No | Enable this property to compress your parquet files. It is necessary to compact parquet files when using Materialized views. |
| Proxy read timeout | spin box | Yes, Analytics service only | Enter the required read timeout for rendering a UI request in seconds. You can adjust the value based on the proxy server configurations. |
| Cleanup Job Refresh Time in Minutes | spin box | Yes, Loader service only | Enter the time interval in minutes for a cleanup job schedule to remove unnecessary files from the shared storage. The interval value cannot be less than 10 minutes. |
UI Customizations
The following table illustrates the configuration properties of the User Interface (UI) customizations:
| Configuration Property | Control | Requires Restart | Description |
|---|---|---|---|
| Color Palette Mode | drop down list | No | Choose a color palette for your insights and dashboards. The options are: ● Sophisticated ● Classic ● Contemporary ● Bright & Bold ● 90s Retro ● Custom |
| Custom Palette Color | text box | No | Enter the HEX codes for 12 colors to be used by the custom palette as follows: #255888, #D1481A, #285F6C,... This property is only available when you set Color Palette Mode to Custom. |
Diagnostics
The following table illustrates the diagnostics configuration property:
| Configuration Property | Control | Requires Restart | Description |
|---|---|---|---|
| Logging | text box | No | Enter the logging level to specify the required information in your log files. Here are the available logging levels in descending order: ● OFF - disables logging ● SEVERE (highest value) - this level logs severe events that can cause termination. ● WARNING ● INFO ● CONFIG ● FINE ● FINER ● FINEST (lowest value) ● ALL - logs all messages The expression must be as follows: component1=level1:component2=level2. For example, you can reduce the level of logging on your production system for the Loader service by setting the logging level to SEVERE. Enter: com.incorta.engine=SEVERE:com.incorta.server=SEVERE:com.incorta.loader=SEVERE |
| Allow Cloud Disk Space Monitoring | toggle | No | Disabled by default. This option applies to cloud storage only, enabling it would monitor the disk space every 5 min which is not recommended as it might increase number of I/O operations. |
Data Agent
The following table illustrates the data agent configuration property:
| Configuration Property | Control | Requires Restart | Description |
|---|---|---|---|
| Enable Data Agent | toggle | Yes, all services | Enable this property to allow Incorta to connect and extract data from one or more data sources behind a firewall. For more information, refer to the Concepts → Data Agent document. |
| Analytics Data Agent Port | spin box | Yes, Analytics service only | Enter the port number of your Analytics data agent. The value cannot be less than 1 or greater than 65535. This property is only available when Enable Data Agent is toggled on. |
| Loader Data Agent Port | spin box | Yes, Loader service only | Enter the port number of your Loader data agent. The value cannot be less than 1 or greater than 65535. This property is only available when Enable Data Agent is toggled on. |
| Analytics Public Hosts and Ports | text box | No | Enter your Analytics public hosts and ports. Note the following: ● Changing the Analytics public hosts and ports requires re-generating the .auth files ● This property is only available when Enable Data Agent is toggled on. |
| Loader Public Hosts and Ports | text box | No | Enter your Loader public hosts and ports. Note the following: ● Changing the Loader public hosts and ports requires re-generating the .auth files ● This property is only available when Enable Data Agent is toggled on. |
The following table illustrates the email configuration properties:
| Configuration Property | Control | Requires Restart | Description |
|---|---|---|---|
| Local Rendering URL Protocol | drop down list | No | Select the local rendering URL protocol: ● http ● https |
| Local Rendering Host | text box | No | Enter the local rendering host. For example, 127.0.0.1. |
| Local Rendering Port | spin box | No | Enter the local rendering port number. For example, 8080. |
| Headless Browser Rendering (in milliseconds) | spin box | No | Enter the number of milliseconds after which rendering a dashboard would timeout. For example, 9000. |
Security
The following table illustrates the security configuration properties:
| Configuration Property | Control | Requires Restart | Description |
|---|---|---|---|
| Enable Iframe inclusion | toggle | No | Enable external websites to host Incorta dashboard(s) in an iFrame. Enabling this option mandates that Incorta is accessed through SSL connections (HTTPS). |