as you would any other Spring Bean. By default the instance will embed a local in-memory server (a Node in Elasticsearch terms) and use the current working directory as the home directory for the server normally have a read-only “master” repository that has a short, but identifiable, prefix (like “metrics.mysystem”), and many write-only repositories with prefixes that start with the master prefix (like metrics metrics.mysystem.* in the example above). It is efficient to read all the keys from a “master” repository like that, but inefficient to read a subset with a longer prefix (e.g. using one of the writing repositories)

as you would any other Spring Bean. By default the instance will embed a local in-memory server (a Node in ElasticSearch terms) and use the current working directory as the home directory for the server normally have a read-only “master” repository that has a short, but identifiable, prefix (like “metrics.mysystem”), and many write-only repositories with prefixes that start with the master prefix (like metrics metrics.mysystem.* in the example above). It is efficient to read all the keys from a “master” repository like that, but inefficient to read a subset with a longer prefix (e.g. using one of the writing repositories)

as you would any other Spring Bean. By default the instance will embed a local in-memory server (a Node in ElasticSearch terms) and use the current working directory as the home directory for the server normally have a read-only “master” repository that has a short, but identifiable, prefix (like “metrics.mysystem”), and many write-only repositories with prefixes that start with the master prefix (like metrics metrics.mysystem.* in the example above). It is efficient to read all the keys from a “master” repository like that, but inefficient to read a subset with a longer prefix (e.g. using one of the writing repositories)

as you would any other Spring Bean. By default the instance will embed a local in-memory server (a Node in Elasticsearch terms) and use the current working directory as the home directory for the server normally have a read-only “master” repository that has a short, but identifiable, prefix (like “metrics.mysystem”), and many write-only repositories with prefixes that start with the master prefix (like metrics metrics.mysystem.* in the example above). It is efficient to read all the keys from a “master” repository like that, but inefficient to read a subset with a longer prefix (e.g. using one of the writing repositories)

as you would any other Spring Bean. By default the instance will embed a local in-memory server (a Node in Elasticsearch terms) and use the current working directory as the home directory for the server normally have a read-only “master” repository that has a short, but identifiable, prefix (like “metrics.mysystem”), and many write-only repositories with prefixes that start with the master prefix (like metrics metrics.mysystem.* in the example above). It is efficient to read all the keys from a “master” repository like that, but inefficient to read a subset with a longer prefix (e.g. using one of the writing repositories)

as you would any other Spring Bean. By default the instance will embed a local in-memory server (a Node in Elasticsearch terms) and use the current working directory as the home directory for the server normally have a read-only “master” repository that has a short, but identifiable, prefix (like “metrics.mysystem”), and many write-only repositories with prefixes that start with the master prefix (like metrics metrics.mysystem.* in the example above). It is efficient to read all the keys from a “master” repository like that, but inefficient to read a subset with a longer prefix (e.g. using one of the writing repositories)

as you would any other Spring Bean. By default the instance will embed a local in-memory server (a Node in Elasticsearch terms) and use the current working directory as the home directory for the server normally have a read-only “master” repository that has a short, but identifiable, prefix (like “metrics.mysystem”), and many write-only repositories with prefixes that start with the master prefix (like metrics metrics.mysystem.* in the example above). It is efficient to read all the keys from a “master” repository like that, but inefficient to read a subset with a longer prefix (e.g. using one of the writing repositories)

as you would any other Spring Bean. By default the instance will embed a local in-memory server (a Node in Elasticsearch terms) and use the current working directory as the home directory for the server normally have a read-only “master” repository that has a short, but identifiable, prefix (like “metrics.mysystem”), and many write-only repositories with prefixes that start with the master prefix (like metrics metrics.mysystem.* in the example above). It is efficient to read all the keys from a “master” repository like that, but inefficient to read a subset with a longer prefix (e.g. using one of the writing repositories)

as you would any other Spring Bean. By default the instance will embed a local in-memory server (a Node in Elasticsearch terms) and use the current working directory as the home directory for the server normally have a read-only “master” repository that has a short, but identifiable, prefix (like “metrics.mysystem”), and many write-only repositories with prefixes that start with the master prefix (like metrics metrics.mysystem.* in the example above). It is efficient to read all the keys from a “master” repository like that, but inefficient to read a subset with a longer prefix (e.g. using one of the writing repositories)

as you would any other Spring Bean. By default the instance will embed a local in-memory server (a Node in Elasticsearch terms) and use the current working directory as the home directory for the server normally have a read-only “master” repository that has a short, but identifiable, prefix (like “metrics.mysystem”), and many write-only repositories with prefixes that start with the master prefix (like metrics metrics.mysystem.* in the example above). It is efficient to read all the keys from a “master” repository like that, but inefficient to read a subset with a longer prefix (e.g. using one of the writing repositories)