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Soluciones de arquitectura alojadas por el cliente: Explicaciones de los componentes

Esta página forma parte de una serie de varias partes en la que se analiza el hosting de Looker, las metodologías de implementación y las prácticas recomendadas para los componentes involucrados. En esta página, se exploran las prácticas comunes para componentes específicos de la arquitectura de Looker y se describe cómo configurarlos en una implementación.

Esta serie consta de tres partes:

Descripción general de la infraestructura alojada por el cliente
Patrones de arquitectura de infraestructura alojados por el cliente
Explicaciones sobre los componentes de infraestructura alojada por el cliente (esta página)

Looker tiene una serie de dependencias para alojar el servidor, prestar servicio a cargas de trabajo ad hoc y programadas, hacer un seguimiento del desarrollo de modelos iterativos, etc. Estas dependencias se denominan componentes en esta página, y cada componente se aborda en detalle en las siguientes secciones:

Configuración del host
Configuración de JVM
Opciones de inicio de Looker
Base de datos interna (backend)
Servicio de Git
Red

Configuración del host

SO y distribución

Looker se ejecuta bien en las versiones más comunes de Linux: Red Hat, SUSE y Debian/Ubuntu. Por lo general, se aceptan los derivados de estas distribuciones que están diseñados y optimizados para ejecutarse en un entorno particular. Por ejemplo, las distribuciones de Google Cloud o AWS de Linux son compatibles con Looker. Debian y Ubuntu es la variedad de Linux más usada en la comunidad de Looker. Estas son las versiones más conocidas de la asistencia de Looker. Es más fácil usar Debian/Ubuntu o un sistema operativo para un proveedor de servicios en la nube específico que esté derivado de Debian/Ubuntu.

Looker se ejecuta en la máquina virtual de Java (JVM). Al elegir una distribución, considera si las versiones de OpenJDK 8 están actualizadas. Es posible que las distribuciones anteriores de Linux puedan ejecutar Looker, pero la versión y las bibliotecas de Java que Looker requiere para funciones específicas deben estar actualizadas. Si la JVM no contiene todas las bibliotecas y versiones recomendadas de Looker, Looker no funcionará normalmente. Actualmente, Looker requiere la actualización 161 de Java HotSpot 1.8 o una posterior, o bien OpenJDK 8 181 y versiones posteriores.

CPU y memoria

4x16 nodos (4 CPU y 16 GB de RAM) son suficientes para un sistema de desarrollo o una instancia de prueba básica de Looker que usa un equipo pequeño. Sin embargo, para el uso en producción, esto no suele ser suficiente. Según nuestra experiencia, 16 x 64 nodos (16 CPU y 64 GB de RAM) representan un buen equilibrio entre precio y rendimiento. Más de 64 GB de RAM pueden afectar el rendimiento, ya que los eventos de recolección de elementos no utilizados tienen un solo subproceso y detiene la ejecución de todos los demás subprocesos.

Almacenamiento en disco

100 GB de espacio en disco suelen ser más que suficientes para un sistema de producción.

Consideraciones del clúster

Looker se ejecuta en una JVM de Java, y Java puede tener dificultades para administrar memorias superiores a 64 GB. Como regla general, si se requiere más capacidad, debe agregar nodos adicionales de 16 × 64 al clúster en lugar de aumentar el tamaño de los nodos a un valor superior a 16 × 64. También es posible que prefieramos usar una arquitectura agrupada en clústeres para aumentar la disponibilidad.

En un clúster, los nodos de Looker deben compartir ciertas partes del sistema de archivos. Los datos compartidos incluyen lo siguiente:

Modelos de LookML
Los modelos de LookML del desarrollador
Conectividad del servidor de Git

Dado que el sistema de archivos es compartido y aloja varios repositorios de Git, el manejo del acceso simultáneo y el bloqueo de archivos es fundamental. El sistema de archivos debe ser compatible con POSIX. Se sabe que el sistema de archivos de red (NFS) funciona y está disponible con Linux. Debes iniciar una instancia adicional de Linux y configurar NFS para compartir el disco. El NFS predeterminado es potencialmente un punto único de fallo, por lo que debes considerar una configuración de conmutación por error o una configuración de alta disponibilidad.

Los metadatos de Looker también deben estar centralizados, por lo que su base de datos interna debe migrarse a MySQL. Puede ser un servicio de MySQL o una implementación dedicada de MySQL. En la sección Base de datos interna (backend) de esta página, se explica con más detalle.

Configuración de JVM

La configuración de JVM de Looker se define dentro de la secuencia de comandos de inicio de Looker. Después de cualquier actualización, Looker debe reiniciarse para que los cambios en el manifiesto Las configuraciones predeterminadas son las siguientes:

java \
  -XX:+UseG1GC -XX:MaxGCPauseMillis=2000 \
  -Xms$JAVAMEM -Xmx$JAVAMEM \
  -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps \
  -Xloggc:/tmp/gc.log ${JAVAARGS} \
  -jar looker.jar start ${LOOKERARGS}

Recursos

La configuración de recursos se define en la secuencia de comandos de inicio de Looker.

JAVAMEM="2300m"
METAMEM="800m"

La asignación de memoria para la JVM debe tener en cuenta la sobrecarga del sistema operativo en la que se ejecuta Looker. La regla general es que a la JVM se le puede asignar hasta un 60% de la memoria total, pero hay advertencias en función del tamaño de la máquina. En el caso de las máquinas con el mínimo de 8 GB de memoria total, recomendamos una asignación de 4 a 5 GB para Java y de 800 MB para Meta. En máquinas más grandes, se puede asignar una proporción menor de memoria al sistema operativo. Por ejemplo, en el caso de máquinas con 60 GB de memoria total, recomendamos una asignación de 36 GB para Java y 1 GB para Meta. Es importante tener en cuenta que la asignación de memoria de Java normalmente debería escalar con la memoria total de la máquina, pero Meta debería ser suficiente con 1 GB.

Además, dado que Looker comparte recursos del sistema con otros procesos como Chromium para el procesamiento, se debe seleccionar la cantidad de memoria asignada a Java en el contexto de la carga de renderización anticipada y el tamaño de la máquina. Si se espera que la carga de representación sea baja, se puede asignar a Java una parte mayor de la memoria total. Por ejemplo, en una máquina con 60 GB de memoria total, Java podría asignarse de forma segura a 46 GB, lo que es más que la recomendación general del 60%. Las asignaciones exactas de recursos adecuadas para cada implementación varían, así que usa el 60% como modelo de referencia y ajústalo según lo indique el uso.

Recolección de elementos no utilizados

Looker prefiere usar el recolector de elementos no utilizados más moderno disponible para su versión de Java. De forma predeterminada, el tiempo de espera de la recolección de elementos no utilizados es de 2 segundos, pero esto se puede cambiar editando la siguiente opción en la configuración de inicio:

-XX:MaxGCPauseMillis=2000

En una máquina más grande con varios núcleos, se puede acortar el tiempo de espera de la recolección de elementos no utilizados.

Registros

De forma predeterminada, los registros de recolección de elementos no utilizados de Looker se almacenan en /tmp/gc.log. Esto se puede cambiar si editas la siguiente opción en la configuración de inicio:

-Xloggc:/tmp/gc.log

JMX

Es posible que la administración de Looker requiera supervisión para definir mejor los recursos. Recomendamos usar JMX para supervisar el uso de memoria de JVM.

Opciones de inicio de Looker

Las opciones de inicio se almacenan en un archivo llamado lookerstart.cfg. Este archivo se origina en la secuencia de comandos de shell que inicia Looker.

Grupos de subprocesos

Looker es una aplicación con varios subprocesos y tiene varios conjuntos de subprocesos. Estos grupos de subprocesos abarcan desde el servidor web principal hasta subservicios especializados, como la programación, el procesamiento y las conexiones de bases de datos externas. Según los flujos de trabajo de tu empresa, es posible que estos grupos deban modificarse desde la configuración predeterminada. En particular, existen consideraciones especiales para las topologías del clúster que se mencionan en la página de prácticas recomendadas Patrones y prácticas de arquitectura de infraestructura alojados por el cliente.

Opciones de alta capacidad de procesamiento para la programación

Para todos los nodos que no son programadores, agrega --scheduler-threads=0 a la variable de entorno LOOKERARGS en lookerstart.cfg. Sin los subprocesos del programador, no se ejecutará ningún trabajo programado en estos nodos.

Para todos los nodos del programador dedicado, agrega --scheduler-threads=<n> a la variable de entorno LOOKERARGS en lookerstart.cfg. Looker inicia con 10 subprocesos de programador de forma predeterminada, pero este valor se puede aumentar a <n>. Con <n> subprocesos de programador, cada nodo podrá ejecutar <n> trabajos de programación simultáneos. Por lo general, se recomienda mantener <n> menos de 2 veces la cantidad de CPU. El host recomendado más grande es uno con 16 CPU y 64 GB de memoria, por lo que el límite superior de subprocesos del programador debe ser inferior a 32.

Opciones de alta capacidad de procesamiento de renderización

Para todos los nodos sin renderización, agrega --concurrent-render-jobs=0 a environment variable in lookerstart.cfg. Without renderer nodes, no render jobs will run on these nodes..




 For all dedicated render nodes, add --concurrent-render-jobs=<n> to the LOOKERARGS environment variable in lookerstart.cfg. Looker starts with two render threads by default, but this can be increased to <n>. With <n> render threads, each node will be capable of executing <n> concurrent render jobs.



  Each render job can utilize a significant amount of memory. Budget about 2 GB per render job. For example, if the core Looker process (Java) is allocated 60% of the total memory and 20% of the remaining memory is reserved for the operating system, that leaves the last 20% for render jobs. On a 64 GB machine, that leaves 12 GB, which is enough for 6 concurrent render jobs. If a node is dedicated to rendering and is not included in the load balancer pool that is handling interactive jobs, the core Looker process memory can be reduced to allow for more render jobs. On a 64 GB machine, one might allocate approximately 30% (20 GB) to the Looker core process. Reserving 20% for general OS use, that leaves 50% (32 GB) for rendering, which is enough for 16 concurrent render jobs.


Internal (backend) database


  The Looker server maintains information about its own configuration, database connections, users, groups, and roles, folders, user-defined Looks and dashboards, and various other data in an internal database.



  For a standalone Looker instance of moderate size, this data is stored within an in-memory HyperSQL database embedded in the Looker process itself. The data for this database is stored in the file <looker install directory>/.db/looker.script. Although convenient and lightweight, this database experiences performance issues with heavy usage. Therefore, we recommend starting with a remote MySQL database. If this isn't feasible, we recommend migration to a remote MySQL database once the ~/looker/.db/looker.script file reaches 600 MB. Clusters must use a MySQL database.



  The Looker server makes many small reads and writes to the MySQL database. Every time a user runs a Look or an Explore, Looker will check the database to verify that the user is still logged in, the user has privileges to access the data, the user has privileges to run the Look or Explore, etc. Looker will also write data to the MySQL database, including the actual SQL that was run, the time the request started and ended, etc. A single interaction between a user and the Looker application could result in 15 or 20 small reads and writes to the MySQL database.


MySQL


  The MySQL server should be version 8.0.x, and must be configured to use utf8mb4 encoding. The InnoDB storage engine must be used. The setup instructions for MySQL, as well as instructions for how to migrate data from an existing HyperSQL database to MySQL, are available on the Migrating the Looker backend database to MySQL documentation page.



 When configuring Looker to use MySQL, a YAML file must be created containing the connection information. Name the YAML file looker-db.yml and add the setting -d looker-db.yml in the LOOKERARGS section of the lookerstart.cfg file.


MariaDB


  MySQL is dual-licensed, available both as open source and as a commercial product. Oracle has continued to enhance MySQL, and MySQL is forked as MariaDB. The MariaDB equivalent versions of MySQL are known to work with Looker, but they aren't developed for or tested by Looker's engineering teams; therefore, functionality is not supported or guaranteed.


Cloud versions


  If you host Looker in your cloud infrastructure, it is logical to host the MySQL database in the same cloud infrastructure. The three major cloud vendors — Amazon AWS, Microsoft Azure, and Google Cloud. The cloud providers manage much of the maintenance and configuration for the MySQL database and offer services to help manage backups, provide rapid recovery, etc. These products are known to work well with Looker.


System Activity queries


  The MySQL database is used to store information about how users are using Looker. Any Looker user who has permission to view the System Activity model has access to a number of prebuilt Looker dashboards to analyze this data. Users can also access Explores of Looker metadata to build additional analysis. The MySQL database is primarily used for small, fast, "operational" queries. The large, slow, "analytic" queries generated by the System Activity model can compete with these operational queries and slow Looker down.



  In these cases, the MySQL database can be replicated to another database. Both self-managed and certain cloud-managed systems provide simple configuration of replication to other databases. Configuring replication is outside the scope of this document.



  In order to use the replica for the System Activity queries, you will create a copy of the looker-db.yml file, for example named looker-usage-db.yml, modify it to point to the replica, and add the setting --internal-analytics-connection-file looker-usage-db.yml
  to the LOOKERARGS section of the lookerstart.cfg file.



  The System Activity queries can run against a MySQL instance or a Google BigQuery database. They are not tested against other databases.


MySQL performance configuration


  In addition to the settings required to migrate the Looker backend database to MySQL, highly active clusters may benefit from additional tuning and configuration. These settings can be made to the /etc/my.cnf file, or through the Cloud Console for cloud-managed instances.



  The my.cnf configuration file is divided into several sections. The setting changes discussed below are made in the [mysqld] section.


Set the InnoDB buffer pool size


  The InnoDB buffer pool size is the total RAM that is used to store the state of the InnoDB data files in memory. If the server is dedicated to running MySQL, the innodb_buffer_pool_size should be set to 50%-70% of total system memory.



  If the total size of the database is small, it is allowable to set the InnoDB buffer pool to the size of the database rather than 50% or more of memory.



  For this example, a server has 64 GB of memory; therefore, the InnoDB buffer pool should be between 32 GB and 45 GB. Bigger is typically better.


[mysqld]
...
innodb_buffer_pool_size=45G


Set the InnoDB buffer pool instances


  When multiple threads attempt to search a large buffer pool, they could contend. To prevent this, the buffer pool is divided into smaller units that can be accessed by different threads without conflict. By default, the buffer pool is divided into 8 instances. This creates the potential for an 8 thread bottleneck. Increasing the number of buffer pool instances reduces the chance of a bottleneck. The innodb_buffer_pool_instances should be set so that each buffer pool gets at least 1 GB of memory.


[mysqld]
...
innodb_buffer_pool_instances=32


Optimize the InnoDB log file


  When a transaction is committed, the database has the option to update the data in the actual file, or it can save details about the transaction in the log. If the database crashes before the data files have been updated, the log file can be "replayed" to apply the changes. Writing to the log file is a simple append operation. It is efficient to append to the log at commit time, then batch up multiple changes to the data files and write them in a single IO operation. When the log file is filled, the database has to pause processing new transactions and write all the changed data back to disk.



  As a general rule of thumb, the InnoDB log file should be large enough to contain 1 hour of transactions.



 There are typically two InnoDB log files. They should be about 25% of your InnoDB buffer pool. For an example database with a 32 GB buffer pool, the InnoDB log files should total 8 GB, or 4 GB per file.


[mysqld]
...
innodb_log_file_size=8GB


Configure InnoDB IO capacity


  MySQL will throttle the speed at which writes are recorded to the disk so as not to overwhelm the server. The default values are conservative for most servers. For best performance use the sysbench utility to measure the random write speed to the data disk, then use that value to configure the IO capacity so that MySQL will write data more quickly.



  On a cloud-hosted system, the cloud vendor should be able to report the performance of the disks used for data storage. For a self-hosted MySQL server, measure the speed of random writes to the data disk in IO operations per second (IOPS). The Linux utility sysbench is one way to measure this. Use that value for the innodb_io_capacity_max, and a value one-half to three-quarters of that for innodb_io_capacity. So, in the example below, we would see the values if we measured 800 IOPS.


[mysqld]
...
innodb_io_capacity=500
innodb_io_capacity_max=800


Configure InnoDB threads


  MySQL will open at least one thread for each client being served. If many clients are connected simultaneously, that can lead to a huge number of threads being processed. This can cause the system to spend more time swapping than processing.



  Benchmarking should be done to determine the ideal number of threads. To test, set the number of threads between the number of CPUs (or CPU cores) on the system and 4x the number of CPUs. For a 16-core system, this value is likely between 16 and 64.


[mysqld]
...
innodb_thread_concurrency=32


Transaction durability


  A transaction value of 1 forces MySQL to write to disk for every transaction. If the server crashes, the transaction won't be lost, but database performance will be impacted. Setting this value to 0 or 2 can improve performance, but it will come at the risk of losing a couple of seconds' worth of data transactions.


[mysqld]
...
innodb_flush_log_at_trx_commit=1


Set the flush method


  The operating system normally does buffering of writes to the disk. Since MySQL and the OS are both buffering, there is a performance penalty. Reducing the flush method one layer of buffering can improve performance.


[mysqld]
...
innodb_flush_method=O_DIRECT


Enable one file per table


  By default, MySQL will use a single data file for all data. The innodb_file_per_table setting will create a separate file for each table, which improves performance and data management.


[mysqld]
...
innodb_file_per_table=ON


Disable stats on metadata


  This setting disables the collection of stats on internal metadata tables, improving read performance.


[mysqld]
...
innodb_stats_on_metadata=OFF


Disable the query cache


  The query cache is deprecated, so setting the query_cache_size and query_cache_type to 0 disables it.


[mysqld]
...
query_cache_size=0
query_cache_type=0


Enlarge the join buffer


  The join_buffer is used to perform joins in memory. Increasing it can improve certain operations.


[mysqld]
...
join_buffer_size=512KB


Enlarge the temporary table and max heap sizes


  The tmp_table_size and max_heap_table_size set reasonable defaults for temporary in-memory tables, before they are forced to disk.


[mysqld
...
tmp_table_size=32MB
max_heap_table_size=32MB


Adjust the table open cache


  The table_open_cache setting determines the size of the cache that holds the file descriptors for open tables. The table_open_cache_instances setting breaks the cache into a number of smaller parts. There is a potential for thread contention in the table_open_cache, so dividing it into smaller parts helps increase concurrency.


[mysqld]
...
table_open_cache=2048
table_open_cache_instances=16


Git service


  Looker is designed to work with a Git service to provide version management of the LookML files. Major Git hosting services are supported, including GitHub, GitLab, Bitbucket, etc. Git service providers offer additional value adds such as a GUI to view code changes and support for workflows like pull requests and change approvals. If required, Git can be run on a plain Linux server.



  If a Git hosting service is not appropriate for your deployment because of security rules, many of these service providers offer versions that can be run in your own environment. GitLab, in particular, is commonly self-hosted and can be used as an open source product with no license cost or as a supported licensed product. GitHub Enterprise is available as a self-hosted service and is a supported commercial product.



  The following sections list nuances for the most common service providers.


GitHub/GitHub Enterprise


  The Setting up and testing a Git connection documentation page uses GitHub as an example.


GitLab/gitlab.com


  Refer to the Using GitLab for version control in Looker Looker Community post for detailed setup steps for GitLab. If your repo is contained within subgroups, these can be added to the repo URL using either the HTTPS or SSH format:


https://gitlab.com/accountname/subgroup/reponame

git@gitlab.com:accountname/subgroup/reponame.git


  Additionally, there are three different ways you can store Looker-generated SSH keys in GitLab: as a user SSH key, as a repository deploy key, and as a global shared deploy key. A more in-depth explanation can be found in the GitLab documentation.


Google Cloud Source


  Refer to the Using Cloud Source Repositories for version control in Looker Community Post for steps to set up Git with Cloud Source Repositories.


Bitbucket Cloud


  Refer to the Using Bitbucket for version control in Looker Community Post for steps for setting up Git with Bitbucket Cloud. As of August 2021, Bitbucket Cloud does not support secrets on deploy webhooks.


Bitbucket Server


  To use pull requests with Bitbucket Server, you may need to complete the following steps:



  
    When you open a pull request, Looker will automatically use the default port number (7999) in the URL. If you are using a custom port number, you will need to replace the port number in the URL manually.
  
  
    You will need to hit the project's deploy webhook to sync the production branch in Looker with the repo's master branch.
  


Phabricator diffusion


  Refer to the Setting up Phabricator and Looker for version control Community Post for steps on setting up Git with Phabricator.


Network

Inbound connections

Looker web application


  By default, Looker listens for HTTPS requests on port 9999. Looker uses a self-signed certificate with a CN of self-signed.looker.com.  The Looker server can alternately be configured to do the following:



  Accept HTTP connections from an SSL-termination load balancer/proxy, with the --ssl-provided-externally-by=<s> startup flag. The value should either be set to the IP address of the proxy, or to a host name that can be locally resolved to the IP address of the proxy. Looker will accept HTTP connections only from this IP address.

 Use a customer supplied SSL certificate, with the --ssl-keystore=<s> startup flag.


Looker API


  The Looker API listens on port 19999. If the installation requires access to the API, then the load balancer should have the requisite forwarding rules. The same SSL considerations apply as with the main web application. We recommend using a distinct port from the web application.


Load balancers


  A load balancer is often used to accept an HTTPS request at port 443 using the customer's certificate, then forward the request to the Looker server node at port 9999 using the self-signed certificate or HTTP. If load balancers are using Looker's self-signed certificate, they must be configured to accept that certificate.


Idle connections and timeouts


  When a user starts a large request in Looker, that could result in a query that could be expensive to run on the database. If the user abandons that request in any way — by shutting the lid on their laptop, disconnecting from the network, killing that tab in the browser, etc. — Looker wants to know and terminate that database query.



  To handle this situation, when the client web application makes a request to run a database query, the browser will open a socket connection via a long-lived HTTP request to the Looker server. This connection will sit open and idle. This socket will get disconnected if the client web application is killed or disconnected in any way. The server will see that disconnect and cancel any related database queries.



  Load balancers often notice these open idle connections and kill them. In order to run Looker effectively, the load balancer must be configured to allow this connection to remain open for as long as the longest query a user might run. A timeout of at least 60 minutes is suggested.


Outbound connections


  Looker servers can have unrestricted outbound access to all resources, including the public internet. This simplifies many tasks, such as installing Chromium, which requires access to the package repositories for the Linux distribution.



  The following are outbound connections that Looker may need to make.


Internal database connection



  By default, MySQL listens for connections on port 3306. The Looker nodes must be able to initiate connections to MySQL on this port. Depending on how the repository is hosted, you may need to traverse a firewall.


External services


  Looker's telemetry and license servers are available via HTTPS on the public internet. Traffic from a Looker node to ping.looker.com:443 and license.looker.com:443 may need to be added to an allowlist.


Data warehouse connections


  Cloud-hosted databases may require a connection via the public internet. For example, if you are using BigQuery, then accounts.google.com:443 and www.googleapis.com:443 may need to be added to an allowlist. If the database is outside of your own infrastructure, consult with your database host for network details.


SMTP services


  By default, Looker sends outgoing mail via SendGrid. That may require adding smtp.sendgrid.net:587 to an allowlist. The SMTP settings can be changed in the configuration to use a different mail handler as well.


Action hubs, action servers, and webhooks


  Many scheduler destinations, in particular webhooks and the ones that are enabled in the Looker Admin panel, involve sending data via HTTPS requests.



  For webhooks, these destinations are specified at runtime by users, and may be contrary to the goal of firewalling outbound connections.
  For an action hub, these requests are sent to actions.looker.com. Details can be found in our Looker Action Hub configuration documentation.
  For other action servers, these requests are sent to the domains specified in the action server's configuration by administrators in the Looker Admin panel.


Proxy server


  If the public internet cannot be reached directly, Looker can be configured to use a proxy server for HTTP(S) requests by adding a line like the following to lookerstart.cfg:


JAVAARGS="-Dhttp.proxyHost=myproxy.example.com
  -Dhttp.proxyPort=8080
  -Dhttp.nonProxyHosts=127.0.0.1|localhost
  -Dhttps.proxyHost=myproxy.example.com
  -Dhttps.proxyPort=8080"


  Note that internode communications happen over HTTPS, so if you use a proxy server and your instance is clustered, you will usually want to add the IPs/host names for all the nodes in the cluster to the Dhttp.nonProxyHosts argument.


Internode communications

Internal host identifier


  Within a cluster, each node must be able to communicate with the other nodes. To allow this, the host name or IP address of each node is specified in the startup configuration. When the node starts up, this value will be written into the MySQL repository. Other members of the cluster can then refer to those values to communicate with this node. To specify the host name or IP address in the startup configuration, add -H node1.looker.example.com to the LOOKERARGS environment variable in lookerstart.cfg.



  Since the host name must be unique per node, the lookerstart.cfg file needs to be unique on each instance. As an alternative to hardcoding the host name or IP address, the command hostname -I or hostname --fqdn  can be used to find these at runtime.   To implement this, add -H $(hostname -I) or -H $(hostname --fqdn) to the LOOKERARGS environment variable in lookerstart.cfg.


Internal ports


  In addition to the ports 9999 and 19999, which are used for the web and API servers, respectively, the cluster nodes will communicate with each other through a message broker service, which uses ports 1551 and 61616. Ports 9999 and 19999 must be open to end-user traffic, but 1551 and 61616 must be open between cluster nodes.