Coping with Failureedit

We’ve said that Elasticsearch can cope when nodes fail, so let’s go ahead and try it out. If we kill the first node, our cluster looks like Figure 6, “Cluster after killing one node”.

Figure 6. Cluster after killing one node

The cluster after killing one node

The node we killed was the master node. A cluster must have a master node in order to function correctly, so the first thing that happened was that the nodes elected a new master: Node 2.

Primary shards 1 and 2 were lost when we killed Node 1, and our index cannot function properly if it is missing primary shards. If we had checked the cluster health at this point, we would have seen status red: not all primary shards are active!

Fortunately, a complete copy of the two lost primary shards exists on other nodes, so the first thing that the new master node did was to promote the replicas of these shards on Node 2 and Node 3 to be primaries, putting us back into cluster health yellow. This promotion process was instantaneous, like the flick of a switch.

So why is our cluster health yellow and not green? We have all three primary shards, but we specified that we wanted two replicas of each primary, and currently only one replica is assigned. This prevents us from reaching green, but we’re not too worried here: were we to kill Node 2 as well, our application could still keep running without data loss, because Node 3 contains a copy of every shard.

If we restart Node 1, the cluster would be able to allocate the missing replica shards, resulting in a state similar to the one described in Figure 5, “Increasing the number_of_replicas to 2”. If Node 1 still has copies of the old shards, it will try to reuse them, copying over from the primary shard only the files that have changed in the meantime.

By now, you should have a reasonable idea of how shards allow Elasticsearch to scale horizontally and to ensure that your data is safe. Later we will examine the life cycle of a shard in more detail.