Hi, I want to try the machine learning features in kibana.But when I create a new job and open it, it shows the error
How can I do ?
thanks a lot!
The error message seems to indicate that you may have insufficient memory available. What is the specification of your cluster?
Does your host have 40GB RAM? What does your elasticsearch.yml file look like?
every node has 16G memory.
# ======================== Elasticsearch Configuration =========================
#
# NOTE: Elasticsearch comes with reasonable defaults for most settings.
# Before you set out to tweak and tune the configuration, make sure you
# understand what are you trying to accomplish and the consequences.
#
# The primary way of configuring a node is via this file. This template lists
# the most important settings you may want to configure for a production cluster.
#
# Please consult the documentation for further information on configuration options:
# https://www.elastic.co/guide/en/elasticsearch/reference/index.html
#
# ---------------------------------- Cluster -----------------------------------
#
# Use a descriptive name for your cluster:
#
cluster.name: Winoc
#
# ------------------------------------ Node ------------------------------------
#
# Use a descriptive name for the node:
#
node.name: ES1
#
# Add custom attributes to the node:
#
#node.attr.rack: r1
#
# ----------------------------------- Paths ------------------------------------
#
# Path to directory where to store the data (separate multiple locations by comma):
#
#path.data: /path/to/data
#
# Path to log files:
#
#path.logs: /path/to/logs
#
# ----------------------------------- Memory -----------------------------------
#
# Lock the memory on startup:
#
#bootstrap.memory_lock: true
#
# Make sure that the heap size is set to about half the memory available
# on the system and that the owner of the process is allowed to use this
# limit.
#
# Elasticsearch performs poorly when the system is swapping the memory.
#
# ---------------------------------- Network -----------------------------------
#
# Set the bind address to a specific IP (IPv4 or IPv6):
#
network.host: 172.30.254.24
#
# Set a custom port for HTTP:
#
#http.port: 9200
#
# For more information, consult the network module documentation.
#
# --------------------------------- Discovery ----------------------------------
#
# Pass an initial list of hosts to perform discovery when new node is started:
# The default list of hosts is ["127.0.0.1", "[::1]"]
#discovery.zen.ping.unicast.hosts: ["172.30.254.24", "172.30.254.23", "172.30.254.28","172.30.254.27"]
#
# Prevent the "split brain" by configuring the majority of nodes (total number of master-eligible nodes / 2 + 1):
#
#discovery.zen.minimum_master_nodes:
#
# For more information, consult the zen discovery module documentation.
#
# ---------------------------------- Gateway -----------------------------------
#
#
# Block initial recovery after a full cluster restart until N nodes are started:
#
#gateway.recover_after_nodes: 3
#
# For more information, consult the gateway module documentation.
#
# ---------------------------------- Various -----------------------------------
#
# Require explicit names when deleting indices:
#
#action.destructive_requires_name: true
xpack.security.enabled: "true"
xpack.security.audit.enabled: "true"Machine learning is not a java process and therefore separate from the Elasticsearch JVM. How much memory it can use is determined through configuration based on the total amount of RAM on the node. By default it is 30% of node RAM. If your node has 16GB of RAM, that means at most 4.8GB can be allocated to ML. The error message indicates that the job you created is estimated to need a bit over 15GB of memory, and can therefore not be run.
Maybe you can revise your job to reduce its estimated memory footprint or create perhaps create a dedicated ML node with more RAM available.
I'm curious as to what your ML job configuration is - can you share?
ML jobs aren't usually that memory intensive, unless they are configured incorrectly/inappropriately (i.e. you've chosen to "split" on a field with a cardinality of +1 million, for example)
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