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Data Distribution Internals

This document discusses how data distribution works in FDB.

Data distribution manages the lifetime of storage servers, decides which storage server is responsible for which data range, and ensures data is evenly distributed across all storage servers (SS). This document discusses the internals of data distribution (DD) from three perspectives: components that are the data structures of DD; operations that are the actors changing the states of DD; and mechanisms that realize functionalities of DD.

Components

Storage server (class TCServerInfo): DD creates a TCServerInfo object for each storage server (SS). The TCServerInfo includes: (1) the SS’ locality, which includes the processID that is unique to ip:port, the zoneId that specifies which rack the SS is on, and the dcId that specifies which DC the SS is in; (2) the server’s teams, which will be discussed in the following paragraph; (3) the tracker that monitor the status of the server; and (4) extra information related to the server’s interface and preference. A server is healthy if its storage engine on the process is the same with the configured storage engine, and it is marked as desired by DD.

Machine (class TCMachineInfo): A machine in FDB is considered as a rack, because a typical FDB cluster will only use one physical host from each rack in the datacenter to reduce the impact of regular rack-maintenance events on the cluster. All servers on the same rack belong to the same machine. A machine is healthy if there exists a healthy server on the machine.

Server team (class TCTeamInfo): A server team is a group of k servers that host the same key ranges, where k is the replication factor that is usually three. A server team is healthy if every server in the team is healthy and those servers’ localities satisfy the replication requirement. Servers are grouped into server teams to reduce the possibility of data unavailability events at the event of k server failures.

Machine team (class TCMachineTeamInfo): A machine team is a group of k machines, where k is the replication factor. Each server team must be on a machine team, meaning that each server in the server team is on a machine in the machine team and that no two servers are on the same machine. Similar to the purpose of server teams, machine teams are used to reduce the possibility of data unavailability events at the event of k machine failures. A machine team is healthy if every machine on the team is healthy and machines’ localities satisfy the replication policy.

TeamCollection: It has a global view of all servers and server teams, machines and machine teams. With the information, it creates server teams and machine teams. It also maintains the configuration settings for DD, which is used to create teams and decide which type of storage servers to recruit.

Shard (struct DDShardInfo): A shard is a key range. A shard is maintained by a server team. A server team is responsible for many shards. Each shard has a similar amount of data. When a shard has too much data or has too much write traffic, it will be split into multiple shards and redistributed to server teams. Likewise, when a shard has too little data, it can be merged with its neighbors.

RelocateShard (struct RelocateShard): A RelocateShard records the key range that need to be moved among servers and the data movement’s priority. DD always move shards with higher priorities first.

Data distribution queue (struct DDQueue): It receives shards to be relocated (i.e., RelocateShards), decides which shard should be moved to which server team, prioritizes the data movement based on relocate shard’s priority, and controls the progress of data movement based on servers’ workload.

Special keys in the system keyspace: DD saves its state in the system keyspace to recover from failure and to ensure every process (e.g., commit proxies, tLogs and storage servers) has a consistent view of which storage server is responsible for which key range.

serverKeys sub-space (\xff/serverKeys/): It records the start key of each shard a server is responsible for. The format is \xff/serverKeys/[serverID]/[start_key]. To get start keys of all shards for a server, DD can read the key range with prefix \xff/serverKeys/[serverID]/ and decode the value of [start_key].

keyServers sub-space (\xff/keyServers/): It records each key’s source and destination server IDs. The format is \xff/keyServers/[start_key]/[src_server][dst_server], where [start_key] is the start key of a shard, [src_server] are the servers responsible for the shard, [dst_server] are the new servers where the shard will be moved to when relocating shard request is initialized. To get all source and destination servers for the shard, DD can read the key range with the prefix \xff/keyServers/[start_key] and decode the value [src_server][dst_server]. To get each shard’s boundary, DD can read the key range with the prefix \xff/keyServers/ and collect all *[start_key]*s. Two consecutive [start_key], say start_key1 and start_key2, construct the key range, say [start_key1, start_key2), for a shard.

moveKeysLockOwnerKey (\xff/moveKeysLock/Owner) and moveKeysLockWriteKey (\xff/moveKeysLock/Write): When DD moves keys, it must grab the moveKeysLock, which consists of an owner key and a write key. The owner key (i.e., moveKeysLockOwnerKey) specifies which DD currently owns the lock. The write key (i.e., moveKeysLockWriteKey) specifies which DD is currently changing the mapping between keys and servers (i.e., operating on serverKeys and keyServers subspace). If DD finds it does not own both keys when it tries to move keys, it will kill itself by throwing an error. The cluster controller will recruit a new one.

When a new DD is initialized, it will set itself as the owner by setting its random UID to the moveKeysLockOwnerKey. Since the owner key has only one value, at most one DD can own the DD-related system subspace. This avoids the potential race condition between multiple DDs which may co-exist during DD recruitment.

Transaction State Store (txnStateStore): It is a replica of the special keyspace that stores the cluster’s states, such as which SS is responsible for which shard. Because commit proxies use txnStateStore to decide which tLog and SS should receive a mutation, commit proxies must have a consistent view of txnStateStore. Therefore, changes to txnStateStore must be populated to all commit proxies in total order. To achieve that, we use the special transaction (applyMetaMutations) to update txnStateStore and use resolvers to ensure the total ordering (serializable snapshot isolation).

Private mutation: A private mutation is a mutation updating a special system key, such as keyServersKey (\xff/keyServers/) and serverKeysKey (\xff/serverKeys/). Like a normal mutation, a private mutation will be processed by the transaction systems (i.e., commit proxy, resolver and tLog) and be routed to a set of storage servers, based on the mutation’s tag, to update the key-value in the storage engine. Private mutations also keep the serializable snapshot isolation and consensus: The results of committed concurrent private mutations can be reproduced by sequentially executing the mutations, and all components in FDB have the same view of the mutations.

Operations

Operations on the states (and data structure) of DD are done in actors. Each actor is responsible for only a specific task. We will describe the most important actors in this section.

Storage server tracker (storageServerTracker): Whenever a storage server is created, a storage server tracker is created for the server. The tracker monitors the status (e.g., healthiness) of the server. When a server becomes unhealthy or the server’s process dies, the tracker issues the request to remove data on the server. Once all data are moved away from the server, the tracker remove the servers’ information from DD. When a server’s storage interface changes -- because the storage process reboots or moved -- the tracker updates the server’s information and changes the server’s teams accordingly to ensure the replication policy is always satisfied.

Team tracker (teamTracker): Whenever a server team is created, a team tracker is created to monitor the healthiness of the team. When a healthy team becomes unhealthy, the team tracker will find all shards on the team, create the RelocateShard requests, and send the requests to the dataDistributionQueue.

Team builder (buildTeams): Team builder is created when DD is initialized. It is invoked by the following events: (a) a new server is created and added to DD; (b) an existing server is removed from DD; (c) there is zero teams in the system.

Whenever the team builder is invoked, it aims to build the desired number of server teams. To ensure each server team belongs to a machine team, it first builds the desired number of machine teams; it then picks a machine team and picks a server from each machine in the machine team to form a server team.

Data distribution queue server (dataDistributionQueue actor): It is created when DD is initialized. It behaves as a server to handle RelocateShard related requests. For example, it waits on the stream of RelocateShard. When a new RelocateShard is sent by teamTracker, it enqueues the new shard, and cancels the inflight shards that overlap with the new relocate shard.

applyMetaMutations: This is special logic to handle private transactions that modify txnStateStore and special system keys. Transaction systems (i.e., commit proxy, resolver and tLogs) and storage servers perform extra operations for the special transactions. For any update, it will be executed on all commit proxies in order so that all commit proxies have a consistent view of the txnStateStore. It will also send special keys to storage servers so that storage servers know the new keyspace they are now responsible for.

A storage server (SS) processes all requests sent to the server in its storageServerCore actor. When a (private) mutation request is sent to a SS, the server will call the update() function. Eventually, the StorageUpdater class will be invoked to apply the mutation in applyMutation() function, which handles private mutations applyPrivateData() function.

If a new key range is assigned to a storage server, the storage server will receive a private mutation that changes the serverKeys (\xff/serverKeys/) and keyServers (\xff/keyServers/). Then the server will create transactions, just like an FDB client, to read key-value pairs in the assigned key range and write the data into its local storage engine.

If a key range is removed from a storage server, similarly the storage server will receive a private mutation that changes the serverKeys and keyServers. Once the private mutation is processed by the SS, the SS removes data in its versioned data.

Mechanisms

How is data distribution initialized?

When a data distribution role is created, it recovers the states of the previous DD from the system keyspace. First, it sets itself as the owner of the moveKeysLock. Then it collects the information of servers and shards, the map between servers and shards, and the replication configuration by reading DD-related system keyspace (i.e., serverKeys sub-space). Based on the information, the new DD recreates its components (e.g., servers, teams, and trackers) that matches the states of the previous DD. Trackers will evaluate the healthiness of servers and teams based on the replication policy. Unhealthy servers and teams will be removed and new servers and teams will be created if the replication policy is changed.

When to move keys?

Keys can be moved from a server to another for several reasons: (1) DD moves keys from disk-overutilized servers to disk-underutilized servers, where a server’s disk-utilization is defined as the server’s disk space usage; (2) DD moves keys from read-busy servers to read-cold servers if read-aware data distribution is enabled; (3) DD splits or merges shards in order to rebalance the disk usage of servers; (4) DD removes redundant teams when the team number is larger than the desired number; (5) DD repairs the replication factor by duplicate shards from a server to another when servers in a team fail.

Actors are created to monitor the reasons of key movement: (1) MountainChopper and ValleyFiller actors periodically measure a random server team’s utilization and rebalance the server’s keys among other servers; (2) shardMerger and shardSplitter actors take a shard as input and respectively evaluates if the input shard can be merged with its neighboring shards without creating a too big shard and if the shard should be split. Once new shards are created, the actors create the shard’s tracker and send RelocateShard requests to DD’s queue; (3) serverTeamRemover and machineTeamRemover actors periodically evaluate if the number of server teams and machine teams is larger than the desired number. If so, they respectively pick a server team or a machine team to remove based on predefined criteria; (4) teamTracker actor monitors a team’s healthiness. When a server in the team becomes unhealthy, it issues the RelocateShard request to repair the replication factor. The less servers a team has, the higher priority the RelocateShard request will be.

Movement Priority

There are roughly 4 class of movement priorities

Healthy priority. The movement is for maintain the cluster healthy status, and the priority is depended on the healthy status of the source team.
Load balance priority. The movement is for balance cluster workload.
Boundary change priority. The movement will change current shard boundaries.
Others. Like resuming a in-flight movement.

Each shard movement has a priority associating with the move attempt, The explanation of each priority knob (PRIORITY_<XXX>) is in ServerKnobs.h.

In status json output, please look at field .data.team_tracker.state for team priority state.

How to move keys?

A key range is a shard. A shard is the minimum unit of moving data. The storage server’s ownership of a shard -- which SS owns which shard -- is stored in the system keyspace serverKeys (\xff/serverKeys/) and keyServers (\xff/keyServers/). To simplify the explanation, we refer to the storage server’s ownership of a shard as a shard’s ownership.

A shard’s ownership is used in transaction systems (commit proxy and tLogs) to route mutations to tLogs and storage servers. When a commit proxy receives a mutation, it uses the shard’s ownership to decide which k tLogs receive the mutation, assuming k is the replias factor. When a storage server pulls mutations from tLogs, it uses the shard’s ownership to decide which shards the SS is responsible for and which tLog the SS should pull the data from.

A shard’s ownership must be consistent across transaction systems and SSes, so that mutations can be correctly routed to SSes. Moving keys from a SS to another requires changing the shard’s ownership under ACID property. The ACID property is achieved by using FDB transactions to change the *serverKeys *(\xff/serverKeys/) and keyServers (\xff/keyServers/). The mutation on the *serverKeys and keyServers *will be categorized as private mutations in transaction system. Compared to normal mutation, the private mutations will change the transaction state store (txnStateStore) that maintains the serverKeys and keyServers for transaction systems (commit proxy and tLog) when it arrives on each transaction component (e.g., tLog). Because mutations are processed in total order with the ACID guarantees, the change to the txnStateStore will be executed in total order on each node and the change on the shard’s ownership will also be consistent.

The data movement from one server (called source server) to another (called destination server) has four steps: (1) DD adds the destination server as the shard’s new owner; (2) The destination server will issue transactions to read the shard range and write the key-value pairs back. The key-value will be routed to the destination server and saved in the server’s storage engine; (3) DD removes the source server from the shard’s ownership by modifying the system keyspace; (4) DD removes the shard’s information owned by the source server from the server’s team information (i.e., shardsAffectedByTeamFailure).

Read-aware Data Distribution

Motivation

Before FDB 7.2, when the data distributor wants to rebalance shard, it only considers write bandwidth when choosing source and destination team, and the moved shard is chosen randomly. There are several cases where uneven read distribution from users causes a small subset of servers to be busy with read requests. This motivates the data distributor considering read busyness to minimize the read load unevenness.

When does read rebalance happen

The data distributor will periodically check whether the read rebalance is needed. The conditions of rebalancing are

the worst CPU usage of source team >= 0.15 , which means the source team is somewhat busy;
the ongoing relocation is less than the parallelism budget. queuedRelocation[ priority ] < countLimit (default 50);
the source team is not throttled to be a data movement source team. ( now() - The last time the source team was selected ) * time volumn (default 20) > read sample interval (2 min default);
the read load difference between source team and destination team is larger than 30% of the source team load;

Metrics definition

READ_LOAD = ceil(READ_BYTES_PER_KSECOND / PAGE_SIZE)
READ_IMBALANCE = ( MAX READ_LOAD / AVG READ_LOAD )
MOVE_SCORE = READ_DENSITY = READ_BYTES_PER_KSECOND / SHARD_BYTE

The aim for read-aware data distributor is to minimize the IMBALANCE while not harm the disk utilization balance.

Which shard to move

Basically, the MountainChopper will handle read-hot shards distribution with following steps:

The MountainChopper chooses the source team with the largest READ_LOAD while it satisfies HARD_CONSTRAINT, then check whether rebalance is needed;
- Hard constraint:
  - Team is healthy
  - The last time this team was source team is larger than (READ_SAMPLE_INTERVAL / MOVEMENT_PER_SAMPLE)
  - The worst CPU usage of source team >= 0.15
Choose the destination team for moving
- Hard constraint:
  - Team is healthy
  - The team’s available space is larger than the median free space
- Goals
  - The destination team has the least LOAD in a random team set while it satisfies HARD_CONSTRAINT;
Select K shards on the source team of which a. LOAD(shard) < (LOAD(src) - LOAD(dest)) * READ_REBALANCE_MAX_SHARD_FRAC ; b. LOAD(shard) > AVG(SourceShardLoad); c. with the highest top-K MOVE_SCORE;

We use 3.a and 3.b to set a eligible shard bandwidth for read rebalance moving. If the upper bound is too large, it’ll just make the hot shard shift to another team but not even the read load. If the upper bound is small, we’ll just move some cold shards to other servers, which is also not helpful. The default value of READ_REBALANCE_MAX_SHARD_FRAC is 0.2 (up to 0.5) which is decided based on skewed workload test.
Issue relocation request to move a random shard in the top k set. If the maximum limit of read-balance movement is reached, give up this relocation.

Note: The ValleyFiller chooses a source team from a random set with the largest LOAD, and a destination team with the least LOAD.

Performance Test and Summary

Metrics to measure

StorageMetrics trace event report “FinishedQueries” which means the current storage server finishes how many read operations. The rate of FinishedQueries is what we measure first. The better the load balance is, the more similar the FinishedQueries rate across all storage servers. CPU utilization. This metric is in a positive relationship with “FinishedQueries rate”. A even “FinishedQueries” generally means even CPU utilization in the read-only scenario.
Data movement size. We want to achieve load balance with as little movement as possible;
StandardDeviation(FinishedQueries). It indicates how much difference read load each storage server has.

Typical Test Setup

120GB data, key=32B, value=200B; Single replica; 8 SS (20%) serves 80% read; 8 SS servers 60% write; 4 servers are both read and write hot; TPS=100000, 7 read/txn + 1 write/txn;

Test Result Summary and Recommendation

With intersected sets of read-hot and write-hot servers, read-aware DD even out the read + write load on the double-hot (be both read and write hot) server, which means the converged write load is similar to disk rebalance only algorithm.
Read-aware DD will balance the read workload under the read-skew scenario. Starting from an imbalance STD(FinishedQueries per minute)=16k,the best result it can achieve is STD(FinishedQueries per minute) = 2k.
The typical movement size under a read-skew scenario is 100M ~ 600M under default KNOB value READ_REBALANCE_MAX_SHARD_FRAC=0.2, READ_REBALANCE_SRC_PARALLELISM = 20. Increasing those knobs may accelerate the converge speed with the risk of data movement churn, which overwhelms the destination and over-cold the source.
The upper bound of READ_REBALANCE_MAX_SHARD_FRAC is 0.5. Any value larger than 0.5 can result in hot server switching.
When needing a deeper diagnosis of the read aware DD, BgDDMountainChopper, and BgDDValleyFiller trace events are where to go.

Data Distribution Diagnosis Q&A

Why Read-aware DD hasn't been triggered when there's a read imbalance?
- Check BgDDMountainChopper, BgDDValleyFiller SkipReason field.

The Read-aware DD is triggered, and some data movement happened, but it doesn't help the read balance. Why?

Need to figure out which server is selected as the source and destination. The information is in BgDDMountainChopper*, BgDDValleyFiller* DestTeam and SourceTeam field.
Also, the DDQueueServerCounter event tells how many times a server being a source or destination (defined in

enum CountType : uint8_t { ProposedSource = 0, QueuedSource, LaunchedSource, LaunchedDest };

) for different relocation reason (Other, RebalanceDisk and so on) in different phase within DD_QUEUE_COUNTER_REFRESH_INTERVAL (default 60) seconds. For example,

<Event Severity="10" Time="1659974950.984176" DateTime="2022-08-08T16:09:10Z" Type="DDQueueServerCounter" ID="0000000000000000" ServerId="0000000000000004" OtherPQSD="0 1 3 2" RebalanceDiskPQSD="0 0 1 4" RebalanceReadPQSD="2 0 0 5" MergeShardPQSD="0 0 1 0" SizeSplitPQSD="0 0 5 0" WriteSplitPQSD="1 0 0 0" ThreadID="9733255463206053180" Machine="0.0.0.0:0" LogGroup="default" Roles="TS" />

RebalanceReadPQSD="2 0 0 5" means server 0000000000000004 has been selected as for read balancing for twice, but it's not queued and executed yet. This server also has been a destination for read balancing for 5 times in the past 1 min. Note that the field will be skipped if all 4 numbers are 0. To avoid spammy traces, if is enabled with knob DD_QUEUE_COUNTER_SUMMARIZE = true, event DDQueueServerCounterTooMany will summarize the unreported servers that involved in launched relocations (aka. LaunchedSource, LaunchedDest count are non-zero):

<Event Severity="10" Time="1660095057.995837" DateTime="2022-08-10T01:30:57Z" Type="DDQueueServerCounterTooMany" ID="0000000000000000" RemainedLaunchedSources="000000000000007f,00000000000000d9,00000000000000e8,000000000000014c,0000000000000028,00000000000000d6,0000000000000067,000000000000003e,000000000000007d,000000000000000a,00000000000000cb,0000000000000106,00000000000000c1,000000000000003c,000000000000016e,00000000000000e4,000000000000013c,0000000000000016,0000000000000179,0000000000000061,00000000000000c2,000000000000005a,0000000000000001,00000000000000c9,000000000000012a,00000000000000fb,0000000000000146," RemainedLaunchedDestinations="0000000000000079,0000000000000115,000000000000018e,0000000000000167,0000000000000135,0000000000000139,0000000000000077,0000000000000118,00000000000000bb,0000000000000177,00000000000000c0,000000000000014d,000000000000017f,00000000000000c3,000000000000015c,00000000000000fb,0000000000000186,0000000000000157,00000000000000b6,0000000000000072,0000000000000144," ThreadID="1322639651557440362" Machine="0.0.0.0:0" LogGroup="default" Roles="TS" />

How to track the lifecycle of a relocation attempt for balancing?
- First find the TraceId fields in BgDDMountainChopper*, BgDDValleyFiller*, which indicates a relocation is triggered.
- (Only when enabled) Find the QueuedRelocation event with the same BeginPair and EndPair as the original TraceId. This means the relocation request is queued.
- Find the RelocateShard event whose BeginPair, EndPair field is the same as TraceId. This event means the relocation is ongoing.

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