Monitored Metrics

Database Availability

Database Availability Percentage

Explanation - For our purposes, we’ve defined the database to be unavailable if any operation (transaction start, read, or commit) cannot be completed within 5 seconds. Currently, we only monitor transaction start (get read version) and commit for this metric. We report the percentage of each minute that the database is not unavailable according to this definition.

How to compute - Because this is a metric where we value precision, we compute it by running external processes that each start a new transaction every 0.5 seconds at system immediate priority and then committing them. Any time we have a delay exceeding 5 seconds, we measure the duration of that downtime. We exclude the last 5 seconds of this downtime, as operations performed during this period don’t satisfy the definition of unavailability above.

How we alert - We do not alert on this metric.

Database Available

Explanation - Reports as a point in time measurement once per minute whether the database is available.

How to compute - This value begins reporting 0 anytime the process described in ‘Database Availability Percentage’ detects an operation taking longer than 5 seconds and resets to 1 whenever an operation completes.

How we alert - We alert immediately whenever this value is 0.

Max Unavailability Seconds

Explanation - Reports the largest period of unavailability overlapping a given minute. For example, a 3 minute unavailability that started half way through a minute will report 30, 90, 150, 180 for the 4 minutes that overlap the unavailability period.

How to compute - The process described in ‘Database Availability Percentage’ tracks and reports this data for each minute of the unavailability period.

How we alert - We do not alert on this metric, though it could possibly be combine with ‘Database Available’ to create an alert that fires when unavailability reaches a minimum duration.

Fault Tolerance

Data Loss Margin

Explanation - Reports the number of fault tolerance domains (e.g. separate Zone IDs) that can be safely lost without data loss. Fault tolerance domains are typically assigned to correspond to something like racks or machines, so for this metric you would be measuring something like the number of racks that could be lost.

How to compute - From status:

cluster.fault_tolerance.max_machine_failures_without_losing_data

How we alert - We do not alert on this metric because the Availability Loss Margin alert captures the same circumstances and more.

Availability Loss Margin

Explanation - Reports the number of fault tolerance domains (e.g. separate Zone IDs) that can be safely lost without indefinite availability loss.

How to compute - From status:

cluster.fault_tolerance.max_machine_failures_without_losing_availability

How we alert - We have 3 different alerts on this metric, some based on a relative measure with expected fault tolerance (e.g. 2 for triple, 1 for double):

  1. Fault tolerance is 2 less than expected (only relevant with at least triple redundancy)
  2. Fault tolerance is 1 less than expected for 3 hours (to allow for self-healing)
  3. Fault tolerance decreases more than 4 times in 1 hour (may indicate flapping)

Maintenance Mode

Explanation - Whether or not maintenance mode has been activated, which treats a zone as failed but doesn’t invoke data movement for it.

How to compute - Maintenance mode is on if the following metric is present in status:

cluster.maintenance_seconds_remaining

How we alert - We do not alert on this metric

Process and Machine Count

Process Count

Explanation - The number of processes in the cluster, not counting excluded processes.

How to compute - Count the number of entries in the cluster.processes array where excluded is not true.

How we alert - We have 3 different alerts on this metric, some based on a relative measure with the expected count:

  1. The process count decreases 5 times in 60 minutes (may indicate flapping)
  2. The process count is less that 70% of expected
  3. The process count does not match expected (low severity notification)

Excluded Process Count

Explanation - The number of processes in the cluster that are excluded.

How to compute - Count the number of entries in the cluster.processes array where excluded is true.

How we alert - We do not alert on this metric.

Expected Process Count

Explanation - The expected number of non-excluded processes in the cluster.

How to compute - We determine this number from how we’ve configured the cluster.

How we alert - We do not alert on this metric.

Machine Count

Explanation - The number of machines in the cluster, not counting excluded machines. This number may not be relevant depending on the environment.

How to compute - Count the number of entries in the cluster.machines array where excluded is not true.

How we alert - We have 3 different alerts on this metric, some based on a relative measure with the expected count:

  1. The machine count decreases 5 times in 60 minutes (may indicate flapping)
  2. The machine count is less that 70% of expected
  3. The machine count does not match expected (low severity notification)

Excluded Machine Count

Explanation - The number of machines in the cluster that are excluded.

How to compute - Count the number of entries in the cluster.machines array where excluded is true.

How we alert - We do not alert on this metric.

Expected Machine Count

Explanation - The expected number of non-excluded machines in the cluster.

How to compute - We determine this number from how we’ve configured the cluster.

How we alert - We do not alert on this metric.

Latencies

GRV Probe Latency

Explanation - The latency to get a read version as measured by the cluster controller’s status latency probe.

How to compute - From status:

cluster.latency_probe.transaction_start_seconds

How we alert - We have multiple alerts at different severities depending on the magnitude of the latency. The specific magnitudes depend on the details of the cluster and the guarantees provided. Usually, we require elevated latencies over multiple minutes (e.g. 2 out of 3) to trigger an alert.

Read Probe Latency

Explanation - The latency to read a key as measured by the cluster controller’s status latency probe. Notably, this will only test a read from a single storage server during any given probe and to only a single team when measured over multiple probes. Data distribution could sometimes change which team is responsible for the probed key.

How to compute - From status:

cluster.latency_probe.read_seconds

How we alert - We have multiple alerts at different severities depending on the magnitude of the latency. The specific magnitudes depend on the details of the cluster and the guarantees provided. Usually, we require elevated latencies over multiple minutes (e.g. 2 out of 3) to trigger an alert.

Commit Probe Latency

Explanation - The latency to commit a transaction as measured by the cluster controller’s status latency probe.

How to compute - From status:

cluster.latency_probe.commit_seconds

How we alert - We have multiple alerts at different severities depending on the magnitude of the latency. The specific magnitudes depend on the details of the cluster and the guarantees provided. Usually, we require elevated latencies over multiple minutes (e.g. 2 out of 3) to trigger an alert.

Client GRV Latency

Explanation - A sampled distribution of get read version latencies as measured on the clients.

How to compute - The use of this functionality is currently not well documented.

How we alert - We do not alert on this metric.

Client Read Latency

Explanation - A sampled distribution of read latencies as measured on the clients.

How to compute - The use of this functionality is currently not well documented.

How we alert - We do not alert on this metric.

Client Commit Latency

Explanation - A sampled distribution of commit latencies as measured on the clients.

How to compute - The use of this functionality is currently not well documented.

How we alert - We do not alert on this metric.

Workload

Transaction Starts per Second

Explanation - The number of read versions issued per second.

How to compute - From status:

cluster.workload.transactions.started.hz

How we alert - We do not alert on this metric.

Conflicts per Second

Explanation - The number of transaction conflicts per second.

How to compute - From status:

cluster.workload.transactions.conflicted.hz

How we alert - We do not alert on this metric.

Commits per Second

Explanation - The number of transactions successfully committed per second.

How to compute - From status:

cluster.workload.transactions.committed.hz

How we alert - We do not alert on this metric.

Conflict Rate

Explanation - The rate of conflicts relative to the total number of committed and conflicted transactions.

How to compute - Derived from the conflicts and commits per second metrics:

conflicts_per_second / (conflicts_per_second + commits_per_second)

How we alert - We do not alert on this metric.

Reads per Second

Explanation - The total number of read operations issued per second to storage servers.

How to compute - From status:

cluster.workload.operations.reads.hz

How we alert - We do not alert on this metric.

Keys Read per Second

Explanation - The total number of keys read per second.

How to compute - From status:

cluster.workload.keys.read.hz

How we alert - We do not alert on this metric.

Bytes Read per Second

Explanation - The total number of bytes read per second.

How to compute - From status:

cluster.workload.bytes.read.hz

How we alert - We do not alert on this metric.

Writes per Second

Explanation - The total number of mutations committed per second.

How to compute - From status:

cluster.workload.operations.writes.hz

How we alert - We do not alert on this metric.

Bytes Written Per Second

Explanation - The total number of mutation bytes committed per second.

How to compute - From status:

cluster.workload.bytes.written.hz

How we alert - We do not alert on this metric.

Recoveries

Cluster Generation

Explanation - The cluster generation increases when there is a cluster recovery (i.e. the write subsystem gets restarted). For a successful recovery, the generation usually increases by 2. If it only increases by 1, that could indicate that a recovery is stalled. If it increases by a lot, that might suggest that multiple recoveries are taking place.

How to compute - From status:

cluster.generation

How we alert - We alert if the generation increases in 5 separate minutes in a 60 minute window.

Cluster Load

Ratekeeper Limit

Explanation - The number of transactions that the cluster is allowing to start per second

How to compute - From status:

cluster.qos.transactions_per_second_limit

How we alert - We do not alert on this metric.

Ratekeeper Batch Priority Limit

Explanation - The number of transactions that the cluster is allowing to start per second above which batch priority transactions will not be allowed to start.

How to compute - From status:

cluster.qos.batch_transactions_per_second_limit

How we alert - We do not alert on this metric.

Ratekeeper Released Transactions

Explanation - The number of transactions that the cluster is releasing per second. If this number is near or above the ratekeeper limit, that would indicate that the cluster is saturated and you may see an increase in the get read version latencies.

How to compute - From status:

cluster.qos.released_transactions_per_second

How we alert - We do not alert on this metric.

Max Storage Queue

Explanation - The largest write queue on a storage server, which represents data being stored in memory that has not been persisted to disk. With the default knobs, the target queue size is 1.0GB, and ratekeeper will start trying to reduce the transaction rate when a storage server’s queue size reaches 900MB. Depending on the replication mode, the cluster allows all storage servers from one fault domain (i.e. ZoneID) to exceed this limit without trying to adjust the transaction rate in order to account for various failure scenarios. Storage servers with a queue that reaches 1.5GB (the e-brake) will stop fetching mutations from the transaction logs until they are able to flush some of their data from memory. As of 6.1, batch priority transactions are limited when the queue size reaches a smaller threshold (default target queue size of 500MB).

How to compute - From status:

cluster.qos.worst_queue_bytes_storage_server

How we alert - We alert when the largest queue exceeds 500MB for 30 minutes in a 60 minute window.

Limiting Storage Queue

Explanation - The largest write queue on a storage server that isn’t being ignored for ratekeeper purposes (see max storage queue for details). If this number is large, ratekeeper will start limiting the transaction rate.

How to compute - From status:

cluster.qos.limiting_queue_bytes_storage_server

How we alert - We alert when the limiting queue exceeds 500MB for 10 consecutive minutes.

Max Log Queue

Explanation - The largest write queue on a transaction log, which represents data that is being stored in memory on the transaction log but has not yet been made durable on all applicable storage servers. With the default knobs, the target queue size is 2.4GB, and ratekeeper will start trying to reduce the transaction rate when a transaction log’s queue size reaches 2.0GB. When the queue reaches 1.5GB, the transaction log will start spilling mutations to a persistent structure on disk, which allows the mutations to be flushed from memory and reduces the queue size. During a storage server failure, you will see the queue size grow to this spilling threshold and ideally hold steady at that point. As of 6.1, batch priority transactions are limited when the queue size reaches a smaller threshold (default target queue size of 1.0GB).

How to compute - From status:

cluster.qos.worst_queue_bytes_log_server

How we alert - We alert if the log queue is notably larger than the spilling threshold (>1.6GB) for 3 consecutive minutes.

Storage Read Queue

Explanation - The number of in flight read requests on a storage server. We track the average and maximum of the queue size over all storage processes in the cluster.

How to compute - From status (storage role only):

cluster.processes.<process_id>.roles[n].query_queue_max

How we alert - We do not alert on this metric.

Storage and Log Input Rates

Explanation - The number of bytes being input to each storage server or transaction log for writes as represented in memory. This includes various overhead for the data structures required to store the data, and the magnitude of this overhead is different on storage servers and logs. This data lives in memory for at least 5 seconds, so if the rate is too high it can result in large queues. We track the average and maximum input rates over all storage processes in the cluster.

How to compute - From status (storage and log roles only):

cluster.processes.<process_id>.roles[n].input_bytes.hz

How we alert - We alert if the log input rate is larger than 80MB/s for 20 out of 60 minutes, which can be an indication that we are using a sizable fraction of our logs’ capacity.

Storage Server Operations and Bytes Per Second

Explanation - We track the number of mutations, mutation bytes, reads, and read bytes per second on each storage server. We use this primarily to track whether a single replica contains a hot shard receiving an outsized number of reads or writes. To do so, we monitor the maximum, average, and “2nd team” rate. Comparing the maximum and 2nd team can sometimes indicate a hot shard.

How to compute - From status (storage roles only):

cluster.processes.<process_id>.roles[n].mutations.hz
cluster.processes.<process_id>.roles[n].mutation_bytes.hz
cluster.processes.<process_id>.roles[n].finished_queries.hz
cluster.processes.<process_id>.roles[n].bytes_queried.hz

To estimate the rate for the 2nd team (i.e the team that is the 2nd busiest in the cluster), we ignore the top replication_factor storage processes.

How we alert - We do not alert on these metrics.

Transaction Log to Storage Server Lag

Explanation - How far behind the latest mutations on the storage servers are from those on the transaction logs, measured in seconds. In addition to monitoring the average and maximum lag, we also measure what we call the “worst replica lag”, which is an estimate of the worst lag for a whole replica of data.

During recoveries of the write subsystem, this number can temporarily increase because the database is advanced by many seconds worth of versions.

When a missing storage server rejoins, if its data hasn’t been re-replicated yet it will appear with a large lag that should steadily decrease as it catches up.

A storage server that ratekeeper allows to exceed the target queue size may eventually start lagging if it remains slow.

How to compute - From status (storage roles only):

cluster.processes.<process_id>.roles[n].data_lag.seconds

To compute the “worst replica lag”, we ignore the lag for all storage servers in the first N-1 fault domains, where N is the minimum number of replicas remaining across all data shards as reported by status at:

cluster.data.state.min_replicas_remaining

How we alert - We alert when the maximum lag exceeds 4 hours for a duration of 2 minutes or if it exceeds 1000 seconds for a duration of 60 minutes. A more sophisticated alert may only alert if the lag is large and not decreasing.

We also alert when the worst replica lag exceeds 15 seconds for 3 consecutive minutes.

Storage Server Durability Lag

Explanation - How far behind in seconds that the mutations on a storage server’s disk are from the latest mutations in that storage server’s memory. A large lag means can mean that the storage server isn’t keeping up with the mutation rate, and the queue size can grow as a result. We monitor the average and maximum durability lag for the cluster.

How to compute - From status (storage roles only):

cluster.process.<process_id>.roles[n].durability_lag.seconds

How we alert - We do not alert on this metric.

Other Cluster Metrics

Data Movement

Explanation - How much data is actively being moved or queued to be moved between shards in the cluster. There is often a small amount of rebalancing movement happening to keep the cluster well distributed, but certain failures and maintenance operations can cause a lot of movement.

How to compute - From status:

cluster.data.moving_data.in_flight_bytes
cluster.data.moving_data.in_queue_bytes

How we alert - We do not alert on this metric

Coordinators

Explanation - The number of coordinators in the cluster, both as configured and that are reachable from our monitoring agent.

How to compute - This list of coordinators can be found in status:

cluster.coordinators.coordinators

Each coordinator in the list also reports if it is reachable:

cluster.coordinators.coordinators.reachable

How we alert - We alert if there are any unreachable coordinators for a duration of 3 hours or more.

Clients

Explanation - A count of connected clients and incompatible clients. Currently, a large number of connected clients can be taxing for some parts of the cluster. Having incompatible clients may indicate a client-side misconfiguration somewhere.

How to compute - The connected client count can be obtained from status directly:

cluster.clients.count

To get the incompatible client count, we read the following list from status and count the number of entries. Note that this is actually a list of incompatible connections, which could theoretically include incompatible server processes:

cluster.incompatible_connections

How we alert - We alert if the number of connected clients exceeds 1500 for 10 minutes. We also have a low priority alert if there are any incompatible connections for a period longer than 3 hours.

Resource Usage

CPU Usage

Explanation - Percentage of available CPU resources being used. We track the average and maximum values for each process (as a fraction of 1 core) and each machine (as a fraction of all logical cores). A useful extension of this would be to track the average and/or max per cluster role to highlight which parts of the cluster are heavily utilized.

How to compute - All of these metrics can be obtained from status. For processes:

cluster.processes.<process_id>.cpu.usage_cores

For machines:

cluster.machines.<machine_id>.cpu.logical_core_utilization

To get the roles assigned to each process:

cluster.processes.<process_id>.roles[n].role

How we alert - We do not alert on this metric.

Disk Activity

Explanation - Various metrics for how the disks are being used. We track averages and maximums for disk reads per second, disk writes per second, and disk busyness percentage.

How to compute - All of these metrics can be obtained from status. For reads:

cluster.processes.<process_id>.disk.reads.hz

For writes:

cluster.processes.<process_id>.disk.writes.hz

For busyness (as a fraction of 1):

cluster.processes.<process_id>.disk.busy

How we alert - We do not alert on this metric.

Memory Usage

Explanation - How much memory is being used by each process and on each machine. We track this in absolute numbers and as a percentage with both averages and maximums.

How to compute - All of these metrics can be obtained from status. For process absolute memory:

cluster.processes.<process_id>.memory.used_bytes

For process memory used percentage, divide used memory by available memory:

cluster.processes.<process_id>.memory.available_bytes

For machine absolute memory:

cluster.machines.<machine_id>.memory.committed_bytes

For machine memory used percentage, divide used memory by free memory:

cluster.machines.<machine_id>.memory.free_bytes

How we alert - We do not alert on this metric.

Network Activity

Explanation - Input and output network rates for processes and machines in megabits per second (Mbps). We track averages and maximums for each.

How to compute - All of these metrics can be obtained from status. For process traffic:

cluster.processes.<process_id>.network.megabits_received.hz
cluster.processes.<process_id>.network.megabits_sent.hz

For machine traffic:

cluster.machines.<machine_id>.network.megabits_received.hz
cluster.machines.<machine_id>.network.megabits_sent.hz

How we alert - We do not alert on this metric.

Network Connections

Explanation - Statistics about open connection and connection activity. For each process, we track the number of connections, the number of connections opened per second, the number of connections closed per second, and the number of connection errors per second.

How to compute - All of these metrics can be obtained from status:

cluster.processes.<process_id>.network.current_connections
cluster.processes.<process_id>.network.connections_established.hz
cluster.processes.<process_id>.network.connections_closed.hz
cluster.processes.<process_id>.network.connection_errors.hz

How we alert - We do not alert on this metric.

Network Retransmits

Explanation - The number of TCP segments retransmitted per second per machine.

How to compute - From status:

cluster.machines.<machine_id>.network.tcp_segments_retransmitted.hz

How we alert - We do not alert on this metric.

Space Usage

Dataset Size

Explanation - The logical size of the database (i.e. the estimated sum of key and value sizes) and the physical size of the database (bytes used on disk). We also report an overhead factor, which is the physical size divided by the logical size. Typically this is marginally larger than the replication factor.

How to compute - From status:

cluster.data.total_kv_size_bytes
cluster.data.total_disk_used_bytes

How we alert - We do not alert on this metric.

Process Space Usage

Explanation - Various metrics relating to the space usage on each process. We track the amount of space free on each process, reporting minimums and averages for absolute bytes and as a percentage. We also track the amount of space available to each process, which includes space within data files that is reusable. For available space, we track the minimum available to storage processes and the minimum available for the transaction logs’ queues and kv-stores as percentages.

Running out of disk space can be a difficult situation to resolve, and it’s important to be proactive about maintaining some buffer space.

How to compute - All of these metrics can be obtained from status. For process free bytes:

cluster.processes.<process_id>.disk.free_bytes

For process free percentage, divide free bytes by total bytes:

cluster.processes.<process_id>.disk.total_bytes

For available percentage divide available bytes by total bytes. The first is for kv-store data structures, present in storage and log roles:

cluster.processes.<process_id>.roles[n].kvstore_available_bytes
cluster.processes.<process_id>.roles[n].kvstore_total_bytes

The second is for the queue data structure, present only in log roles:

cluster.processes.<process_id>.roles[n].queue_disk_available_bytes
cluster.processes.<process_id>.roles[n].queue_disk_total_bytes

How we alert - We alert when free space on any process falls below 15%. We also alert with low severity when available space falls below 35% and with higher severity when it falls below 25%.

Cluster Disk Space

Explanation - An accounting of the amount of space on all disks in the cluster as well as how much of that space is free and available, counted separately for storage and log processes. Available space has the same meaning as described in the “Process Space Usage” section above, as measured on each process’s kv-store.

How to compute - This needs to be aggregated from metrics in status. For storage and log roles, the per-process values can be obtained from:

cluster.processes.<process_id>.roles[n].kvstore_total_bytes
cluster.processes.<process_id>.roles[n].kvstore_free_bytes
cluster.processes.<process_id>.roles[n].kvstore_available_bytes

To compute totals for the cluster, these numbers would need to be summed up across all processes in the cluster for each role. If you have multiple processes sharing a single disk, then you can use the locality API to tag each process with an identifier for its disk and then read them back out with:

cluster.processes.<process_id>.locality.<identifier_name>

In this case, you would only count the total and free bytes once per disk. For available bytes, you would add free bytes once per disk and (available-free) for each process.

How we alert - We do not alert on this metric.

Backup and DR

Num Backup/DR Agents Running

Explanation - A count of the number of backup and DR agents currently connected to the cluster. For DR agents, we track the number of DR agents where the cluster in question is the destination cluster, but you could also count the number of agents using the cluster as a source if needed.

How to compute - From status:

cluster.layers.backup.instances_running
cluster.layers.dr_backup.instances_running
cluster.layers.dr_backup_dest.instances_running

How we alert - We have a low severity alert if this number differs at all from the expected value. We have high severity alerts if the number of running agents is less than half of what is expected or if the count decreases 5 times in one hour.

Num Backup/DR Agents Expected

Explanation - The expected numbers of backup and DR agents in the cluster.

How to compute - We determine this number from how we’ve configured the cluster.

How we alert - We do not alert on this metric.

Backup/DR Running

Explanation - Tracks whether backup or DR is running on a cluster. For our purposes, we only report DR is running on the primary cluster.

How to compute - From status:

cluster.layers.backup.tags.default.running_backup
cluster.layers.dr_backup.tags.default.running_backup

How we alert - We alert if backup is not running for 5 consecutive minutes or DR is not running for 15 consecutive minutes. Because we only run backup on primary clusters in a DR pair, we don’t have either of these alerts on secondary clusters.

Backup/DR Rate

Explanation - The rate at which backup and DR are processing data. We report rates for both ranges (i.e. copying data at rest) and new mutations.

How to compute - We can get the total number of bytes of each type in status:

cluster.layers.backup.tags.default.range_bytes_written
cluster.layers.backup.tags.default.mutation_log_bytes_written
cluster.layers.dr_backup.tags.default.range_bytes_written
cluster.layers.dr_backup.tags.default.mutation_log_bytes_written

To compute a rate, it is necessary to query these values multiple times and divide the number of bytes that each has increased by the time elapsed between the queries.

How we alert - See Backup/DR Lag section, where we have an alert that incorporates rate data.

Backup/DR Lag

Explanation - How many seconds behind the most recent mutations a restorable backup or DR is. A backup or DR is restorable if it contains a consistent snapshot of some version of the database. For a backup or DR that is not running or restorable, we do not track lag.

How to compute - From status, you can get the lag from:

cluster.layers.backup.tags.default.last_restorable_seconds_behind
cluster.layers.dr_backup.tags.default.seconds_behind

This would then be combined with whether the backup or DR is running as described above and whether it is restorable:

cluster.layers.backup.tags.default.running_backup_is_restorable
cluster.layers.dr_backup.tags.default.running_backup_is_restorable

How we alert - We have a low severity alert for a backup that is 30 minutes behind and a DR that is 5 minutes behind. We have high severity alerts for a backup or DR that is 60 minutes behind.

We also have a high severity alert if a backup or DR is behind by at least 5 minutes and the total backup/DR rate (combined range and mutation bytes) is less than 1000 bytes/s. For backup, this alert occurs after being in this state for 30 minutes, and for DR it is after 3 minutes.

Backup Seconds Since Last Restorable

Explanation - Measures how many seconds of data have not been backup up and could not be restored.

How to compute - This uses the same source metric as in backup lag, except that we also track it in cases where the backup is not running or is not restorable:

cluster.layers.backup.tags.default.last_restorable_seconds_behind

How we alert - We do not alert on this metric.

Datacenter Lag Seconds

Explanation - When running a multi-DC cluster with async replication, this tracks the lag in seconds between datacenters. It is conceptually similar to DR lag when replication is done between 2 distinct clusters.

How to compute - This information can be obtained from status. The metric used varies depending on the version. In 6.1 and older, use the following metric and divide by 1,000,000:

cluster.datacenter_version_difference

In 6.2 and later, use:

cluster.datacenter_lag.seconds

How we alert - We have not yet defined alerts on this metric.

Estimated Backup Size

Explanation - This is not being tracked correctly.

Process Uptime

Explanation - How long each process has been running.

How to compute - From status:

cluster.processes.<process_id>.uptime_seconds

How we alert - We do not alert on this metric.

Cluster Health

Explanation - This is a complicated metric reported by status that is used to indicate that something about the cluster is not in a desired state. For example, a cluster will not be healthy if it is unavailable, is missing replicas of some data, has any running processes with errors, etc. If the metric indicates the cluster isn’t healthy, running status in fdbcli can help determine what’s wrong.

How to compute - From status:

cluster.database_status.healthy

If the metric is missing, its value is presumed to be false.

How we alert - We do not alert on this metric.

Layer Status

Explanation - Backup and DR report their statistics through a mechanism called “layer status”. If this layer status is missing or invalid, the state of backup and DR cannot be determined. This metric can be used to track whether the layer status mechanism is working.

How to compute - From status:

cluster.layers._valid

If the metric is missing, its value is presumed to be false.

How we alert - We alert if the layer status is invalid for 10 minutes.

Process Errors

Explanation - We track all errors logged by any process running in the cluster (including the backup and DR agents).

How to compute - From process trace logs, look for events with Severity=“40”

How we alert - We receive a daily summary of all errors.

Process Notable Warnings

Explanation - We track all notable warnings logged by any process running in the cluster (including the backup and DR agents). Note that there can be some noise in these events, so we heavily summarize the results.

How to compute - From process trace logs, look for events with Severity=“30”

How we alert - We receive a daily summary of all notable warnings.