Kibana is a visual interface that allows you to explore, visualize, and create dashboards for your log data. Integrating Kibana with Manticore Search can speed up the loading of Kibana visualizations by up to 3 times compared to Elasticsearch, as demonstrated in this demo. This integration enables users to seamlessly analyze their data using interactive dashboards, custom visualizations, and real-time search capabilities. It also simplifies handling diverse data sources by supporting tools like Logstash and Filebeat for streamlined data ingestion, making it a great choice for log analysis workflows.

1. Download Kibana: Ensure you download a Kibana version compatible with Manticore. Currently, version 7.6.0 is tested and recommended. Other 7.x versions may work but could introduce issues. Version 8.x is not supported.
2. Verify Manticore: Ensure your Manticore instance is running and its HTTP API is reachable (default: http://localhost:9308).

1. Open the Kibana configuration file (kibana.yml).
2. Set the URL of your Manticore instance:

   elasticsearch.hosts: ["http://localhost:9308"]

3. Start Kibana and open it in your browser at http://localhost:5601. Replace localhost with your server's IP or hostname if necessary.

Note: Manticore does not require authentication setup when working with Kibana.

• Use the Discover tab in Kibana to search and filter your data interactively.
• Refine your searches using the query bar with simple queries in the Kibana query language.

• Navigate to Visualizations to create custom visualizations:
  • Create a table pattern (it’s called an 'index pattern' in Kibana) if one doesn’t already exist to define your data source.
  • Choose a visualization type (e.g., bar chart, line chart, or pie chart).
  • Configure your visualization, execute it, and explore your data.
  • Save your visualizations for future use.

• Access Dashboards to create or view interactive dashboards:
  • Add visualizations, filters, or controls for a personalized experience.
  • Interact with your data directly from the dashboard.
  • Save dashboards for future use.

• Go to Management > Kibana to customize settings like default time zones and visualization preferences.

• Currently, Kibana version 7.6.0 is tested and recommended. Other 7.x versions may work but could cause issues. Versions 8.x are not supported.
• The following Elasticsearch-specific field types are not supported:
  • Spatial data types
  • Structured data types
  • Document ranking types
  • Text search types (except for plain 'text')
  • Relational data types
• Metric aggregation functions are limited to those supported by Manticore.
• The following Kibana tools are not supported:
  • Canvas – A visualization and presentation tool for combining data with colors and images.
  • Elastic Maps – A tool for analyzing geographical data.
  • Metrics – An app for monitoring infrastructure metrics.
  • Logs – A console-like display for exploring logs from common services.
  • Monitoring:
    • Uptime – Monitors the status of network endpoints via HTTP/S, TCP, and ICMP.
    • APM (Application Performance Monitoring) – Collects in-depth performance metrics from applications.
    • SIEM (Security Information and Event Management) – An interactive workspace for security teams to triage events and conduct initial investigations.
    • ILM (Index lifecycle management) - Automatically manage indices according to performance, resiliency, and retention requirements.
    • Stack Monitoring – Provides visualizations of monitoring data across the Elastic Stack.
  • Elasticsearch Management – A UI for managing Elastic Stack objects, including ILM (Index Lifecycle Management), etc.

Integrate Manticore with tools like Logstash, Filebeat, Fluentbit, or Vector.dev to ingest data from sources like web logs. Once the data is loaded into Manticore, you can explore and visualize it in Kibana.

NOTE: this functionality requires Manticore Buddy. If it doesn't work, make sure Buddy is installed.

Manticore Search can seamlessly consume messages from a Kafka broker, allowing for real-time data indexing and search.

To get started, you need to:

1. Define the source: Specify the Kafka topic from which Manticore Search will read messages. This setup includes details like the broker’s host, port, and topic name.
2. Set up the destination table: Choose a Manticore real-time table to store the incoming Kafka data.
3. Create a materialized view: Set up a materialized view (mv) to handle data transformation and mapping from Kafka to the destination table in Manticore Search. Here, you’ll define field mappings, data transformations, and any filters or conditions for the incoming data stream.

CREATE SOURCE <source name> [(column type, ...)] [source_options]

allowed_field_name 'original JSON key name with special symbols' type

price_field '$price' float    -- maps JSON key '$price' to field 'price_field'
field_123 '123field' text     -- maps JSON key '123field' to field 'field_123'

CREATE SOURCE kafka
(id bigint, term text, abbrev '$abbrev' text, GlossDef json)
type='kafka'
broker_list='kafka:9092'
topic_list='my-data'
consumer_group='manticore'
num_consumers='2'
batch=50

Query OK, 2 rows affected (0.02 sec)

CREATE TABLE destination_kafka
(id bigint, name text, short_name text, received_at text, size multi);

Query OK, 0 rows affected (0.02 sec)

CREATE MATERIALIZED VIEW <materialized view name>
TO <destination table name> AS
SELECT [column|function [as <new name>], ...] FROM <source name>

CREATE MATERIALIZED VIEW view_table
TO destination_kafka AS
SELECT id, term as name, abbrev as short_name,
       UTC_TIMESTAMP() as received_at, GlossDef.size as size FROM kafka

Query OK, 2 rows affected (0.02 sec)

Data is transferred from Kafka to Manticore Search in batches, which are cleared after each run. For calculations across batches, such as AVG, use caution, as these may not work as expected due to batch-by-batch processing.

Here's a mapping table based on the examples above:

SHOW SOURCES

+-------+
| name  |
+-------+
| kafka |
+-------+

SHOW SOURCE kafka;

+--------+-------------------------------------------------------------------+
| Source | Create Table                                                      |
+--------+-------------------------------------------------------------------+
| kafka  | CREATE SOURCE kafka                                               |
|        | (id bigint, term text, abbrev '$abbrev' text, GlossDef json)      |
|        | type='kafka'                                                      |
|        | broker_list='kafka:9092'                                          |
|        | topic_list='my-data'                                              |
|        | consumer_group='manticore'                                        |
|        | num_consumers='2'                                                 |
|        | batch=50                                                          |
+--------+-------------------------------------------------------------------+

SHOW MVS

+------------+
| name       |
+------------+
| view_table |
+------------+

SHOW MV view_table

+------------+--------------------------------------------------------------------------------------------------------+-----------+
| View       | Create Table                                                                                           | suspended |
+------------+--------------------------------------------------------------------------------------------------------+-----------+
| view_table | CREATE MATERIALIZED VIEW view_table TO destination_kafka AS                                            | 0         |
|            | SELECT id, term as name, abbrev as short_name, UTC_TIMESTAMP() as received_at, GlossDef.size as size   |           |
|            | FROM kafka                                                                                             |           |
+------------+--------------------------------------------------------------------------------------------------------+-----------+

ALTER MATERIALIZED VIEW view_table suspended=1

Query OK (0.02 sec)

Kafka offsets commit after each batch or when processing times out. If the process stops unexpectedly during a materialized view query, you may see duplicate entries. To avoid this, include an id field in your schema, allowing Manticore Search to prevent duplicates in the table.

• Worker initialization: After configuring a source and materialized view, Manticore Search sets up a dedicated worker to handle data ingestion from Kafka.
• Message mapping: Messages are mapped according to the source configuration schema, transforming them into a structured format.
• Batching: Messages are grouped into batches for efficient processing. Batch size can be adjusted to suit your performance and latency needs.
• Buffering: Mapped data batches are stored in a buffer table for efficient bulk operations.
• Materialized view processing: The view logic is applied to data in the buffer table, performing any transformations or filtering.
• Data transfer: Processed data is then transferred to the destination real-time table.
• Cleanup: The buffer table is cleared after each batch, ensuring it’s ready for the next set of data.

NOTE: The integration with DBeaver requires Manticore Buddy. If it doesn't work, make sure Buddy is installed.

DBeaver is a SQL client software application and a database administration tool. For MySQL databases, it applies the JDBC application programming interface to interact with them via a JDBC driver.

Manticore allows you to use DBeaver for working with data stored in Manticore tables the same way as if it was stored in a MySQL database.

To start working with Manticore in DBeaver, follow these steps:

• Choose the New database connection option in DBeaver's UI
• Choose SQL -> MySQL as DBeaver's database driver
• Set the Server host and Port options corresponding to the host and port of your Manticore instance (keep the database field empty)
• Set root/<empty password> as authentication credentials

Since Manticore does not fully support MySQL, only a part of DBeaver's functionality is available when working with Manticore.

You will be able to:

• View, create, delete, and rename tables
• Add and drop table columns
• Insert, delete, and update column data

You will not be able to:

• Use database integrity check mechanisms (MyISAM will be set as the only storage engine available)
• Use MySQL procedures, triggers, events, etc.
• Manage database users
• Set other database administration options

Some MySQL data types are not currently supported by Manticore and, therefore, cannot be used when creating a new table with DBeaver. Also, a few of the supported data types are converted to the most similar Manticore types with type precision being ignored in such conversion. Below is the list of supported MySQL data types as well as the Manticore types they are mapped to:

• BIGINT UNSIGNED => bigint
• BOOL => boolean
• DATE, DATETIME, TIMESTAMP => timestamp
• FLOAT => float
• INT => int
• INT UNSIGNED, SMALLINT UNSIGNED, TINYINT UNSIGNED, BIT => uint
• JSON => json
• TEXT, LONGTEXT, MEDIUMTEXT, TINYTEXT, BLOB, LONGBLOB, MEDIUMBLOB, TINYBLOB => text
• VARCHAR, LONG VARCHAR, BINARY, CHAR, VARBINARY, LONG VARBINARY => string

You can find more details about Manticore data types here.

Manticore is able to handle the DATE, DATETIME and TIMESTAMP data types, however, this reqiures Manticore's Buddy enabled. Otherwise, an attempt to operate with one of these types will result in an error.

Note that the TIME type is not supported.

• DBeaver's Preferences -> Connections -> Client identification option must not be turned off or overridden. To work correctly with DBeaver, Manticore needs to distinguish its requests from others. For this, it uses client notification info sent by DBeaver in request headers. Disabling client notification will break that detection and, therefore, Manticore's correct functionality.

• When trying to update data in your table for the first time, you'll see the No unique key popup message and will be asked to define a custom unique key. When you get this message, perform the following steps:

  • Choose the Custom Unique Key option
  • Choose only the id column in the columns list
  • Press Ok

  After that, you'll be able to update your data safely.

NOTE: The integration with Apache Superset requires Manticore Buddy. If it doesn't work, make sure Buddy is installed.

Apache Superset is a modern data exploration and visualization platform. Manticore Search can be integrated with Apache Superset, allowing users to harness the full potential of Manticore Search for data analysis and visualization.

Note: This integration works only with Apache Superset 3.x branch and is not yet available for version 4 or newer.

Before integrating Manticore Search with Apache Superset, ensure that:

1. Manticore Search (version 6.2.0 or later) is properly installed and configured on your server. Refer to the official Manticore Search installation guide for assistance.
2. Apache Superset is set up on your system. Follow the official Apache Superset installation guide for installation instructions.

To connect Manticore Search to Apache Superset:

1. Log in to your Apache Superset dashboard.
2. Click on "+" in the top navigation bar and choose "Database Connections".
3. Click "+ DATABASE" to add a new database.
4. Select "MySQL" as the database type.
5. In the configuration page, provide the following details:
   • Database Name: A name for the database (e.g., "Manticore")
   • Host: Your Manticore Search server host
   • Port: The port on which Manticore Search is running
   • Username: Your Manticore Search username
   • Display Name: A display name for the connection
6. Click "Connect" to verify the connection and save.

After connecting Manticore Search to Apache Superset, you can create charts and dashboards using your Manticore data:

1. In the Apache Superset dashboard, click on "Dashboards" in the top navigation bar and select "New dashboard".
2. Open the dashboard and click on the "+" icon to add a new chart.
3. Choose the dataset connected to Manticore Search.
4. Select the type of chart you want to create (e.g., bar chart, line chart, pie chart).
5. Use Apache Superset's query builder or write an SQL query to fetch data from your Manticore Search database.
6. Customize the chart's appearance, labels, and other settings as needed.
7. Click "UPDATE CHART" to update the chart.
8. Add a name for the chart and click "Save" to permanently save it in the Dashboard.

When working with Manticore Search through Apache Superset, you can:

• View and query data from Manticore Search tables
• Create various types of visualizations based on your Manticore data
• Build and customize dashboards using these visualizations

Manticore Search data types are mapped to MySQL data types when working with Apache Superset. Refer to the Manticore Search documentation for more details on supported data types and their mappings.

• Some advanced MySQL features may not be available when working with Manticore Search through Apache Superset.
• Certain data manipulation operations might be limited compared to working directly with MySQL databases.

By following these steps and guidelines, you can effectively integrate Manticore Search with Apache Superset for powerful data exploration and visualization capabilities.

Please refer to the following resources for more information on Apache Superset and Manticore Search:

• Apache Superset Integration