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Chart version: 2.0.1
Api version: v1
App version: 2.2.1
Hyperledger Fabric Orderer chart (these charts are forked from ...
Chart Type
Set me up:
helm repo add center
Install Chart:
helm install hlf-ord center/owkin/hlf-ord
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Hyperledger Fabric Orderer

Hyperledger Fabric Orderer is the node type responsible for “consensus” for the Hyperledger Fabric permissioned blockchain framework.


$ helm install stable/hlf-ord


The Hyperledger Fabric Orderer can be installed as either a solo orderer (for development), a kafka orderer (for crash fault tolerant consensus) or a etcdraft orderer (etcd to maintain the state and raft wich is Leader/Follower type consensus algorithm).

This Orderer can receive transaction endorsements and package them into blocks to be distributed to the nodes of the Hyperledger Fabric network.

Learn more about deploying a production ready consensus framework based on Apache Kafka. Minimally, you will need to set these options:

  "default.replication.factor": 4  # given a 4 node Kafka cluster
  "unclean.leader.election.enable": false
  "min.insync.replicas": 3  # to permit one Kafka replica to go offline
  "message.max.bytes": "103809024"  # 99 * 1024 * 1024 B
  "replica.fetch.max.bytes": "103809024"  # 99 * 1024 * 1024 B
  "": -1  # Since we need to keep logs indefinitely for the HL Fabric Orderer

Learn more about deploying a production consensus framework based on etcd and raft

You can also check this to migrate from kafka to raft :


  • Kubernetes 1.9+
  • PV provisioner support in the underlying infrastructure.
  • K8S secrets containing:
    • the crypto-materials (e.g. signcert, key, cacert, and optionally intermediatecert, CA credentials)
    • the genesis block for the Orderer
    • the certificate of the Orderer Organisation Admin
  • A running Kafka Chart if you are using the kafka consensus mechanism.

Installing the Chart

To install the chart with the release name ord1:

$ helm install stable/hlf-ord --name ord1

The command deploys the Hyperledger Fabric Orderer on the Kubernetes cluster in the default configuration. The Configuration section lists the parameters that can be configured during installation.

Custom parameters

Specify each parameter using the --set key=value[,key=value] argument to helm install. For example:

$ helm install stable/hlf-ord --name ord1 --set ord.mspID=MyMSP

Alternatively, a YAML file can be provided while installing the chart. This file specifies values to override those provided in the default values.yaml. For example,

$ helm install stable/hlf-ord --name ord1 -f my-values.yaml

Updating the chart

To update the chart run:

$ helm upgrade ord1 stable/hlf-ord -f my-values.yaml

Uninstalling the Chart

To uninstall/delete the ord1 deployment:

$ helm delete ord1

The command removes all the Kubernetes components associated with the chart and deletes the release.


The following table lists the configurable parameters of the Hyperledger Fabric Orderer chart and default values.

Parameter Description Default
image.repository hlf-ord image repository hyperledger/fabric-orderer
image.tag hlf-ord image tag x86_64-1.1.0
image.pullPolicy Image pull policy IfNotPresent
service.port TCP port 7050
service.type K8S service type exposing ports, e.g. ClusterIP ClusterIP
service.portMetrics TCP port for the metrics service 9443
ingress.enabled If true, Ingress will be created false
ingress.annotations Ingress annotations {}
ingress.path Ingress path /
ingress.hosts Ingress hostnames []
ingress.tls Ingress TLS configuration []
persistence.accessMode Use volume as ReadOnly or ReadWrite ReadWriteOnce
persistence.annotations Persistent Volume annotations {}
persistence.size Size of data volume (adjust for production!) 1Gi
persistence.storageClass Storage class of backing PVC default
ord.type Type of Orderer (solo, etcdraft or kafka) solo
ord.mspID ID of MSP the Orderer belongs to OrdererMSP
ord.tls.server.enabled Do we enable server-side TLS? false
ord.tls.client.enabled Do we enable client-side TLS? false
ord.metrics.provider Metrics provider, can be statsd, prometheus or disabled disabled Network type, can be udp or tcp udp
ord.metrics.statsd.address Address of the StatsD server
ord.metrics.statsd.writeInterval Intervall at whitch counters and gauges are pushed 30s
ord.metrics.statsd.prefix Prefix prepended to all the exported metrics
secrets.ord.cred Credentials: ‘CA_USERNAME’ and ‘CA_PASSWORD’
secrets.ord.cert Certificate: as ‘cert.pem’
secrets.ord.key Private key: as ‘key.pem’
secrets.ord.caCert CA Cert: as ‘cacert.pem’
secrets.ord.intCaCert Int. CA Cert: as ‘intermediatecacert.pem’
secrets.ord.tls TLS secret: as ‘tls.crt’ and ‘tls.key’
secrets.ord.tlsRootCert TLS root CA certificate: as ‘cert.pem’
secrets.ord.tlsClient TLS client secret: as ‘tls.crt’ and ‘tls.key’
secrets.ord.tlsClientRootCert TLS client root CA certificate: as ‘cert.pem’
secrets.genesis Secret containing Genesis Block for orderer
secrets.adminCert Secret containing Orderer Org admin certificate
resources CPU/Memory resource requests/limits {}
nodeSelector Node labels for pod assignment {}
tolerations Toleration labels for pod assignment []
affinity Affinity settings for pod assignment {}


The volume stores the Fabric Orderer data and configurations at the /var/hyperledger path of the container.

The chart mounts a Persistent Volume at this location. The volume is created using dynamic volume provisioning through a PersistentVolumeClaim managed by the chart.

Upgrading from version 1.1.x

Previous versions of this chart performed enrollment with the Fabric CA directly from the pod. This prevented the possibility of using development cryptographic material (certificates and keys) from Cryptogen or the usage of other CA mechanisms.

Instead, crypto-material and CA credentials are stored separately as secrets.

If you used the former type of chart, you will need to obtain the relevant credentials and cryptographic material from the running pod, and save it externally to a set of secrets, whose names you will need to feed into the chart, under the secrets.ord section.

An example upgrade procedure is described in

Feedback and feature requests

This is a work in progress and we are happy to accept feature requests. We are even happier to accept pull requests implementing improvements :-)