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# About
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This document is an updated version of the original design documents
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by Spencer Kimball from early 2014.
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# Overview
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Cockroach is a distributed key:value datastore (SQL and structured
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data layers of cockroach have yet to be defined) which supports **ACID
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transactional semantics** and **versioned values** as first-class
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features. The primary design goal is **global consistency and
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survivability**, hence the name. Cockroach aims to tolerate disk,
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machine, rack, and even **datacenter failures** with minimal latency
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disruption and **no manual intervention**. Cockroach nodes are
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symmetric; a design goal is **homogenous deployment** (one binary) with
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minimal configuration.
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Cockroach implements a **single, monolithic sorted map** from key to
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value where both keys and values are byte strings (not unicode).
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Cockroach **scales linearly** (theoretically up to 4 exabytes (4E) of
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logical data). The map is composed of one or more ranges and each range
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is backed by data stored in [RocksDB](http://rocksdb.org/) (a
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variant of LevelDB), and is replicated to a total of three or more
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cockroach servers. Ranges are defined by start and end keys. Ranges are
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merged and split to maintain total byte size within a globally
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configurable min/max size interval. Range sizes default to target `64M` in
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order to facilitate quick splits and merges and to distribute load at
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hotspots within a key range. Range replicas are intended to be located
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in disparate datacenters for survivability (e.g. `{ US-East, US-West,
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Japan }`, `{ Ireland, US-East, US-West}`, `{ Ireland, US-East, US-West,
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Japan, Australia }`).
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Single mutations to ranges are mediated via an instance of a distributed
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consensus algorithm to ensure consistency. We’ve chosen to use the
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[Raft consensus algorithm](https://raftconsensus.github.io); all consensus
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state is stored in RocksDB.
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A single logical mutation may affect multiple key/value pairs. Logical
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mutations have ACID transactional semantics. If all keys affected by a
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logical mutation fall within the same range, atomicity and consistency
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are guaranteed by Raft; this is the **fast commit path**. Otherwise, a
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**non-locking distributed commit** protocol is employed between affected
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ranges.
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Cockroach provides [snapshot isolation](http://en.wikipedia.org/wiki/Snapshot_isolation) (SI) and
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serializable snapshot isolation (SSI) semantics, allowing **externally
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consistent, lock-free reads and writes**--both from a historical
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snapshot timestamp and from the current wall clock time. SI provides
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lock-free reads and writes but still allows write skew. SSI eliminates
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write skew, but introduces a performance hit in the case of a
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contentious system. SSI is the default isolation; clients must
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consciously decide to trade correctness for performance. Cockroach
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implements [a limited form of linearizability](#linearizability),
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providing ordering for any observer or chain of observers.
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Similar to
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[Spanner](http://static.googleusercontent.com/media/research.google.com/en/us/archive/spanner-osdi2012.pdf)
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directories, Cockroach allows configuration of arbitrary zones of data.
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This allows replication factor, storage device type, and/or datacenter
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location to be chosen to optimize performance and/or availability.
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Unlike Spanner, zones are monolithic and don’t allow movement of fine
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grained data on the level of entity groups.
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# Architecture
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Cockroach implements a layered architecture. The highest level of
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abstraction is the SQL layer (currently unspecified in this document).
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It depends directly on the [*structured data
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API*](#structured-data-api), which provides familiar relational concepts
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such as schemas, tables, columns, and indexes. The structured data API
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in turn depends on the [distributed key value store](#key-value-api),
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which handles the details of range addressing to provide the abstraction
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of a single, monolithic key value store. The distributed KV store
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communicates with any number of physical cockroach nodes. Each node
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contains one or more stores, one per physical device.
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Each store contains potentially many ranges, the lowest-level unit of
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key-value data. Ranges are replicated using the Raft consensus protocol.
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The diagram below is a blown up version of stores from four of the five
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nodes in the previous diagram. Each range is replicated three ways using
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raft. The color coding shows associated range replicas.
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Each physical node exports a RoachNode service. Each RoachNode exports
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one or more key ranges. RoachNodes are symmetric. Each has the same
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binary and assumes identical roles.
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Nodes and the ranges they provide access to can be arranged with various
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physical network topologies to make trade offs between reliability and
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performance. For example, a triplicated (3-way replica) range could have
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each replica located on different:
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- disks within a server to tolerate disk failures.
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- servers within a rack to tolerate server failures.
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- servers on different racks within a datacenter to tolerate rack power/network failures.
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- servers in different datacenters to tolerate large scale network or power outages.
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Up to `F` failures can be tolerated, where the total number of replicas `N = 2F + 1` (e.g. with 3x replication, one failure can be tolerated; with 5x replication, two failures, and so on).
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# Cockroach Client
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In order to support diverse client usage, Cockroach clients connect to
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any node via HTTPS using protocol buffers or JSON. The connected node
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proxies involved client work including key lookups and write buffering.
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# Keys
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Cockroach keys are arbitrary byte arrays. If textual data is used in
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keys, utf8 encoding is recommended (this helps for cleaner display of
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values in debugging tools). User-supplied keys are encoded using an
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ordered code. System keys are either prefixed with null characters (`\0`
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or `\0\0`) for system tables, or take the form of
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`<user-key><system-suffix>` to sort user-key-range specific system
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keys immediately after the user keys they refer to. Null characters are
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used in system key prefixes to guarantee that they sort first.
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# Versioned Values
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Cockroach maintains historical versions of values by storing them with
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associated commit timestamps. Reads and scans can specify a snapshot
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time to return the most recent writes prior to the snapshot timestamp.
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Older versions of values are garbage collected by the system during
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compaction according to a user-specified expiration interval. In order
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to support long-running scans (e.g. for MapReduce), all versions have a
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minimum expiration.
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Versioned values are supported via modifications to RocksDB to record
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commit timestamps and GC expirations per key.
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the low water mark of the cache appropriately. If a new range replica leader
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is elected, it sets the low water mark for the cache to the current
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# Lock-Free Distributed Transactions
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Cockroach provides distributed transactions without locks. Cockroach
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transactions support two isolation levels:
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- snapshot isolation (SI) and
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- *serializable* snapshot isolation (SSI).
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*SI* is simple to implement, highly performant, and correct for all but a
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handful of anomalous conditions (e.g. write skew). *SSI* requires just a touch
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more complexity, is still highly performant (less so with contention), and has
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no anomalous conditions. Cockroach’s SSI implementation is based on ideas from
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the literature and some possibly novel insights.
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SSI is the default level, with SI provided for application developers
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who are certain enough of their need for performance and the absence of
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write skew conditions to consciously elect to use it. In a lightly
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contended system, our implementation of SSI is just as performant as SI,
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requiring no locking or additional writes. With contention, our
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implementation of SSI still requires no locking, but will end up
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aborting more transactions. Cockroach’s SI and SSI implementations
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prevent starvation scenarios even for arbitrarily long transactions.
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See the [Cahill paper](https://drive.google.com/file/d/0B9GCVTp_FHJIcEVyZVdDWEpYYXVVbFVDWElrYUV0NHFhU2Fv/edit?usp=sharing)
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for one possible implementation of SSI. This is another [great paper](http://cs.yale.edu/homes/thomson/publications/calvin-sigmod12.pdf).
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For a discussion of SSI implemented by preventing read-write conflicts
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(in contrast to detecting them, called write-snapshot isolation), see
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the [Yabandeh paper](https://drive.google.com/file/d/0B9GCVTp_FHJIMjJ2U2t6aGpHLTFUVHFnMTRUbnBwc2pLa1RN/edit?usp=sharing),
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which is the source of much inspiration for Cockroach’s SSI.
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Each Cockroach transaction is assigned a random priority and a
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"candidate timestamp" at start. The candidate timestamp is the
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provisional timestamp at which the transaction will commit, and is
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chosen as the current clock time of the node coordinating the
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transaction. This means that a transaction without conflicts will
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usually commit with a timestamp that, in absolute time, precedes the
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actual work done by that transaction.
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In the course of coordinating a transaction between one or more
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distributed nodes, the candidate timestamp may be increased, but will
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never be decreased. The core difference between the two isolation levels
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SI and SSI is that the former allows the transaction's candidate
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timestamp to increase and the latter does not.
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in the [Hybrid Logical Clock paper](http://www.cse.buffalo.edu/tech-reports/2014-04.pdf).
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HLC time uses timestamps which are composed of a physical component (thought of
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as and always close to local wall time) and a logical component (used to
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distinguish between events with the same physical component). It allows us to
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track causality for related events similar to vector clocks, but with less
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overhead. In practice, it works much like other logical clocks: When events
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are received by a node, it informs the local HLC about the timestamp supplied
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with the event by the sender, and when events are sent a timestamp generated by
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the local HLC is attached.
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For a more in depth description of HLC please read the paper. Our
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implementation is [here](https://github.com/cockroachdb/cockroach/blob/master/util/hlc/hlc.go).
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Cockroach picks a Timestamp for a transaction using HLC time. Throughout this
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document, *timestamp* always refers to the HLC time which is a singleton
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on each node. The HLC is updated by every read/write event on the node, and
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the HLC time >= walltime. A read/write timestamp received in a cockroach request
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from another node is not only used to version the operation, but also updates
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the HLC on the node. This is useful in guaranteeing that all data read/written
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on a node is at a timestamp < next HLC time.
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1. Start the transaction by writing a new entry to the system
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transaction table (keys prefixed by *\0tx*) with state “PENDING”. In
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parallel write an "intent" value for each datum being written as part
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of the transaction. These are normal MVCC values, with the addition of
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a special flag (i.e. “intent”) indicating that the value may be
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the transaction id (unique and chosen at tx start time by client)
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is stored with intent values. The tx id is used to refer to the
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transaction table when there are conflicts and to make
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tie-breaking decisions on ordering between identical timestamps.
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original candidate timestamp in the absence of read/write conflicts);
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the client selects the maximum from amongst all write timestamps as the
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final commit timestamp.
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transaction table (keys prefixed by *\0tx*). The value of the
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commit entry contains the candidate timestamp (increased as
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necessary to accommodate any latest read timestamps). Note that
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the transaction is considered fully committed at this point and
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control may be returned to the client.
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In the case of an SI transaction, a commit timestamp which was
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increased to accommodate concurrent readers is perfectly
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acceptable and the commit may continue. For SSI transactions,
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however, a gap between candidate and commit timestamps
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necessitates transaction restart (note: restart is different than
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abort--see below).
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After the transaction is committed, all written intents are upgraded
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in parallel by removing the “intent” flag. The transaction is
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considered fully committed before this step and does not wait for
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it to return control to the transaction coordinator.
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In the absence of conflicts, this is the end. Nothing else is necessary
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to ensure the correctness of the system.
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**Conflict Resolution**
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Things get more interesting when a reader or writer encounters an intent
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record or newly-committed value in a location that it needs to read or
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write. This is a conflict, usually causing either of the transactions to
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abort or restart depending on the type of conflict.
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***Transaction restart:***
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This is the usual (and more efficient) type of behaviour and is used
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except when the transaction was aborted (for instance by another
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transaction).
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In effect, that reduces to two cases; the first being the one outlined
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above: An SSI transaction that finds upon attempting to commit that
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its commit timestamp has been pushed. The second case involves a transaction
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actively encountering a conflict, that is, one of its readers or writers
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encounter data that necessitate conflict resolution
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(see transaction interactions below).
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When a transaction restarts, it changes its priority and/or moves its
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begins anew reusing the same tx id. The prior run of the transaction might
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have written some write intents, which need to be deleted before the
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transaction commits, so as to not be included as part of the transaction.
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These stale write intent deletions are done during the reexecution of the
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the same keys as part of the reexecution of the transaction, or explicitly,
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by cleaning up stale intents that are not part of the reexecution of the
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transaction. Since most transactions will end up writing to the same keys,
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the explicit cleanup run just before committing the transaction is usually
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a NOOP.
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***Transaction abort:***
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This is the case in which a transaction, upon reading its transaction
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table entry, finds that it has been aborted. In this case, the
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transaction can not reuse its intents; it returns control to the client
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before cleaning them up (other readers and writers would clean up
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dangling intents as they encounter them) but will make an effort to
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clean up after itself. The next attempt (if applicable) then runs as a
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There are several scenarios in which transactions interact:
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- **Reader encounters write intent or value with newer timestamp far
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enough in the future**: This is not a conflict. The reader is free
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to proceed; after all, it will be reading an older version of the
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value and so does not conflict. Recall that the write intent may
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be committed with a later timestamp than its candidate; it will
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never commit with an earlier one. **Side note**: if a SI transaction
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reader finds an intent with a newer timestamp which the reader’s own
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- **Reader encounters write intent or value with newer timestamp in the
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near future:** In this case, we have to be careful. The newer
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intent may, in absolute terms, have happened in our read's past if
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the clock of the writer is ahead of the node serving the values.
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In that case, we would need to take this value into account, but
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we just don't know. Hence the transaction restarts, using instead
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a future timestamp (but remembering a maximum timestamp used to
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limit the uncertainty window to the maximum clock skew). In fact,
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this is optimized further; see the details under "choosing a time
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stamp" below.
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- **Reader encounters write intent with older timestamp**: the reader
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must follow the intent’s transaction id to the transaction table.
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If the transaction has already been committed, then the reader can
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just read the value. If the write transaction has not yet been
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committed, then the reader has two options. If the write conflict
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is from an SI transaction, the reader can *push that transaction's
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commit timestamp into the future* (and consequently not have to
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read it). This is simple to do: the reader just updates the
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transaction’s commit timestamp to indicate that when/if the
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transaction does commit, it should use a timestamp *at least* as
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high. However, if the write conflict is from an SSI transaction,
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the reader must compare priorities. If the reader has the higher priority,
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it pushes the transaction’s commit timestamp (that
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transaction will then notice its timestamp has been pushed, and
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a new priority `max(new random priority, conflicting txn’s
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priority, the writer aborts the conflicting transaction. If the write
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intent has a higher or equal priority the transaction retries, using as a new
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priority *max(new random priority, conflicting txn’s priority - 1)*;
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the retry occurs after a short, randomized backoff interval.
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- **Writer encounters newer committed value**:
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The committed value could also be an unresolved write intent made by a
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transaction that has already committed. The transaction restarts. On restart,
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the same priority is reused, but the candidate timestamp is moved forward
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to the encountered value's timestamp.
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candidate timestamp is earlier than the low water mark on the cache itself
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(i.e. its last evicted timestamp) or if the key being written has a read
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timestamp later than the write’s candidate timestamp, this later timestamp
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value is returned with the write. A new timestamp forces a transaction
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restart only if it is serializable.
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**Transaction management**
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Transactions are managed by the client proxy (or gateway in SQL Azure
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parlance). Unlike in Spanner, writes are not buffered but are sent
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directly to all implicated ranges. This allows the transaction to abort
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quickly if it encounters a write conflict. The client proxy keeps track
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of all written keys in order to resolve write intents asynchronously upon
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transaction completion. If a transaction commits successfully, all intents
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are upgraded to committed. In the event a transaction is aborted, all written
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intents are deleted. The client proxy doesn’t guarantee it will resolve intents.
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In the event the client proxy restarts before the pending transaction is
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transaction table until aborted by another transaction. Transactions
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heartbeat the transaction table every five seconds by default.
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Transactions encountered by readers or writers with dangling intents
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which haven’t been heartbeat within the required interval are aborted.
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An exploration of retries with contention and abort times with abandoned
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transaction is
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[here](https://docs.google.com/document/d/1kBCu4sdGAnvLqpT-_2vaTbomNmX3_saayWEGYu1j7mQ/edit?usp=sharing).
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**Transaction Table**
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Please see [proto/data.proto](https://github.com/cockroachdb/cockroach/blob/master/proto/data.proto) for the up-to-date structures, the best entry point being `message Transaction`.
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**Pros**
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- No requirement for reliable code execution to prevent stalled 2PC
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protocol.
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- Readers never block with SI semantics; with SSI semantics, they may
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abort.
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- Lower latency than traditional 2PC commit protocol (w/o contention)
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because second phase requires only a single write to the
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transaction table instead of a synchronous round to all
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transaction participants.
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- Priorities avoid starvation for arbitrarily long transactions and
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always pick a winner from between contending transactions (no
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mutual aborts).
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- Writes not buffered at client; writes fail fast.
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- No read-locking overhead required for *serializable* SI (in contrast
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to other SSI implementations).
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- Well-chosen (i.e. less random) priorities can flexibly give
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probabilistic guarantees on latency for arbitrary transactions
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(for example: make OLTP transactions 10x less likely to abort than
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low priority transactions, such as asynchronously scheduled jobs).
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**Cons**
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- Reads from non-leader replicas still require a ping to the leader to
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- Abandoned transactions may block contending writers for up to the
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heartbeat interval, though average wait is likely to be
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considerably shorter (see [graph in link](https://docs.google.com/document/d/1kBCu4sdGAnvLqpT-_2vaTbomNmX3_saayWEGYu1j7mQ/edit?usp=sharing)).
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This is likely considerably more performant than detecting and
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restarting 2PC in order to release read and write locks.
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- Behavior different than other SI implementations: no first writer
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wins, and shorter transactions do not always finish quickly.
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Element of surprise for OLTP systems may be a problematic factor.
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- Aborts can decrease throughput in a contended system compared with
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two phase locking. Aborts and retries increase read and write
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traffic, increase latency and decrease throughput.
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**Choosing a Timestamp**
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A key challenge of reading data in a distributed system with clock skew
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is choosing a timestamp guaranteed to be greater than the latest
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timestamp of any committed transaction (in absolute time). No system can
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claim consistency and fail to read already-committed data.
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accessing a single node is easy. The timestamp is assigned by the node
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itself, so it is guaranteed to be at a greater timestamp than all the
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existing timestamped data on the node.
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For multiple nodes, the timestamp of the node coordinating the
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transaction `t` is used. In addition, a maximum timestamp `t+ε` is
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supplied to provide an upper bound on timestamps for already-committed
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data (`ε` is the maximum clock skew). As the transaction progresses, any
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data read which have timestamps greater than `t` but less than `t+ε`
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cause the transaction to abort and retry with the conflicting timestamp
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the same. This implies that transaction restarts due to clock uncertainty
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can only happen on a time interval of length `ε`.
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into account t<sub>c</sub>, but the timestamp of the node at the time
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of the uncertain read t<sub>node</sub>. The larger of those two timestamps
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t<sub>c</sub> and t<sub>node</sub> (likely equal to the latter) is used
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to increase the read timestamp. Additionally, the conflicting node is
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marked as “certain”. Then, for future reads to that node within the
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transaction, we set `MaxTimestamp = Read Timestamp`, preventing further
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uncertainty restarts.
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Correctness follows from the fact that we know that at the time of the read,
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there exists no version of any key on that node with a higher timestamp than
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encounters a key with a higher timestamp, it knows that in absolute time,
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the value was written after t<sub>node</sub> was obtained, i.e. after the
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uncertain read. Hence the transaction can move forward reading an older version
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of the data (at the transaction's timestamp). This limits the time uncertainty
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restarts attributed to a node to at most one. The tradeoff is that we might
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pick a timestamp larger than the optimal one (> highest conflicting timestamp),
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resulting in the possibility of a few more conflicts.
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We expect retries will be rare, but this assumption may need to be
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revisited if retries become problematic. Note that this problem does not
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apply to historical reads. An alternate approach which does not require
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retries makes a round to all node participants in advance and
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chooses the highest reported node wall time as the timestamp. However,
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knowing which nodes will be accessed in advance is difficult and
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potentially limiting. Cockroach could also potentially use a global
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clock (Google did this with [Percolator](https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Peng.pdf)),
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which would be feasible for smaller, geographically-proximate clusters.
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# Linearizability
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First a word about [***Spanner***](http://research.google.com/archive/spanner.html).
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By combining judicious use of wait intervals with accurate time signals,
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Spanner provides a global ordering between any two non-overlapping transactions
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(in absolute time) with \~14ms latencies. Put another way:
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Spanner guarantees that if a transaction T<sub>1</sub> commits (in absolute time)
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before another transaction T<sub>2</sub> starts, then T<sub>1</sub>'s assigned commit
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timestamp is smaller than T<sub>2</sub>'s. Using atomic clocks and GPS receivers,
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Spanner reduces their clock skew uncertainty to \< 10ms (`ε`). To make
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good on the promised guarantee, transactions must take at least double
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the clock skew uncertainty interval to commit (`2ε`). See [*this
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article*](http://www.cs.cornell.edu/~ie53/publications/DC-col51-Sep13.pdf)
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for a helpful overview of Spanner’s concurrency control.
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Cockroach could make the same guarantees without specialized hardware,
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at the expense of longer wait times. If servers in the cluster were
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configured to work only with NTP, transaction wait times would likely to
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be in excess of 150ms. For wide-area zones, this would be somewhat
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mitigated by overlap from cross datacenter link latencies. If clocks
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were made more accurate, the minimal limit for commit latencies would
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improve.
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However, let’s take a step back and evaluate whether Spanner’s external
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consistency guarantee is worth the automatic commit wait. First, if the
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commit wait is omitted completely, the system still yields a consistent
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view of the map at an arbitrary timestamp. However with clock skew, it
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would become possible for commit timestamps on non-overlapping but
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causally related transactions to suffer temporal reverse. In other
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words, the following scenario is possible for a client without global
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ordering:
505
506
- Start transaction T<sub>1</sub> to modify value `x` with commit time *s<sub>1</sub>*
507
508
- On commit of T<sub>1</sub>, start T<sub>2</sub> to modify value `y` with commit time
509
\> s<sub>2</sub>
510
511
- Read `x` and `y` and discover that s<sub>1</sub> \> s<sub>2</sub> (**!**)
512
513
The external consistency which Spanner guarantees is referred to as
514
**linearizability**. It goes beyond serializability by preserving
515
information about the causality inherent in how external processes
516
interacted with the database. The strength of Spanner’s guarantee can be
517
formulated as follows: any two processes, with clock skew within
518
expected bounds, may independently record their wall times for the
519
completion of transaction T<sub>1</sub> (T<sub>1</sub><sup>end</sup>) and start of transaction
520
T<sub>2</sub> (T<sub>2</sub><sup>start</sup>) respectively, and if later
521
compared such that T<sub>1</sub><sup>end</sup> \< T<sub>2</sub><sup>start</sup>,
522
then commit timestamps s<sub>1</sub> \< s<sub>2</sub>.
523
This guarantee is broad enough to completely cover all cases of explicit
524
causality, in addition to covering any and all imaginable scenarios of implicit
525
causality.
526
527
Our contention is that causality is chiefly important from the
528
perspective of a single client or a chain of successive clients (*if a
529
tree falls in the forest and nobody hears…*). As such, Cockroach
530
provides two mechanisms to provide linearizability for the vast majority
531
of use cases without a mandatory transaction commit wait or an elaborate
532
system to minimize clock skew.
533
534
1. Clients provide the highest transaction commit timestamp with
535
successive transactions. This allows node clocks from previous
536
transactions to effectively participate in the formulation of the
537
commit timestamp for the current transaction. This guarantees
538
linearizability for transactions committed by this client.
539
540
Newly launched clients wait at least 2 \* ε from process start
541
time before beginning their first transaction. This preserves the
542
same property even on client restart, and the wait will be
543
mitigated by process initialization.
544
545
All causally-related events within Cockroach maintain
546
linearizability.
547
548
2. Committed transactions respond with a commit wait parameter which
549
represents the remaining time in the nominal commit wait. This
550
will typically be less than the full commit wait as the consensus
551
write at the coordinator accounts for a portion of it.
552
553
Clients taking any action outside of another Cockroach transaction
554
(e.g. writing to another distributed system component) can either
555
choose to wait the remaining interval before proceeding, or
556
alternatively, pass the wait and/or commit timestamp to the
557
execution of the outside action for its consideration. This pushes
558
the burden of linearizability to clients, but is a useful tool in
559
mitigating commit latencies if the clock skew is potentially
560
large. This functionality can be used for ordering in the face of
561
backchannel dependencies as mentioned in the
562
[AugmentedTime](http://www.cse.buffalo.edu/~demirbas/publications/augmentedTime.pdf)
563
paper.
564
565
Using these mechanisms in place of commit wait, Cockroach’s guarantee can be
566
formulated as follows: any process which signals the start of transaction
567
T<sub>2</sub> (T<sub>2</sub><sup>start</sup>) after the completion of
568
transaction T<sub>1</sub> (T<sub>1</sub><sup>end</sup>), will have commit
569
timestamps such thats<sub>1</sub> \< s<sub>2</sub>.
570
571
# Logical Map Content
572
573
Logically, the map contains a series of reserved system key / value
574
pairs covering accounting, range metadata, node accounting and
575
permissions before the actual key / value pairs for non-system data
576
(e.g. the actual meat of the map).
577
578
- `\0\0meta1` Range metadata for location of `\0\0meta2`.
579
- `\0\0meta1<key1>` Range metadata for location of `\0\0meta2<key1>`.
580
- ...
581
- `\0\0meta1<keyN>`: Range metadata for location of `\0\0meta2<keyN>`.
582
- `\0\0meta2`: Range metadata for location of first non-range metadata key.
583
- `\0\0meta2<key1>`: Range metadata for location of `<key1>`.
584
- ...
585
- `\0\0meta2<keyN>`: Range metadata for location of `<keyN>`.
586
- `\0acct<key0>`: Accounting for key prefix key0.
587
- ...
588
- `\0acct<keyN>`: Accounting for key prefix keyN.
589
- `\0node<node-address0>`: Accounting data for node 0.
590
- ...
591
- `\0node<node-addressN>`: Accounting data for node N.
592
- `\0perm<key0><user0>`: Permissions for user0 for key prefix key0.
593
- ...
594
- `\0perm<keyN><userN>`: Permissions for userN for key prefix keyN.
595
- `\0tree_root`: Range key for root of range-spanning tree.
596
- `\0tx<tx-id0>`: Transaction record for transaction 0.
597
- ...
598
- `\0tx<tx-idN>`: Transaction record for transaction N.
599
- `\0zone<key0>`: Zone information for key prefix key0.
600
- ...
601
- `\0zone<keyN>`: Zone information for key prefix keyN.
602
- `<>acctd<metric0>`: Accounting data for Metric 0 for empty key prefix.
603
- ...
604
- `<>acctd<metricN>`: Accounting data for Metric N for empty key prefix.
605
- `<key0>`: `<value0>` The first user data key.**
606
- ...
607
- `<keyN>`: `<valueN>` The last user data key.**
608
609
There are some additional system entries sprinkled amongst the
610
non-system keys. See the Key-Prefix Accounting section in this document
611
for further details.
612
613
# Node Storage
614
615
Nodes maintain a separate instance of RocksDB for each disk. Each
616
RocksDB instance hosts any number of ranges. RPCs arriving at a
617
RoachNode are multiplexed based on the disk name to the appropriate
618
RocksDB instance. A single instance per disk is used to avoid
619
contention. If every range maintained its own RocksDB, global management
620
of available cache memory would be impossible and writers for each range
621
would compete for non-contiguous writes to multiple RocksDB logs.
622
623
In addition to the key/value pairs of the range itself, various range
624
metadata is maintained.
625
626
- range-spanning tree node links
627
628
- participating replicas
629
630
- consensus metadata
631
632
- split/merge activity
633
634
A really good reference on tuning Linux installations with RocksDB is
635
[here](http://docs.basho.com/riak/latest/ops/advanced/backends/leveldb/).
636
637
# Range Metadata
638
639
The default approximate size of a range is 64M (2\^26 B). In order to
640
support 1P (2\^50 B) of logical data, metadata is needed for roughly
641
2\^(50 - 26) = 2\^24 ranges. A reasonable upper bound on range metadata
643
locations and 220 bytes for the range key itself*). 2\^24 ranges \* 2\^8
644
B would require roughly 4G (2\^32 B) to store--too much to duplicate
645
between machines. Our conclusion is that range metadata must be
646
distributed for large installations.
647
648
To keep key lookups relatively fast in the presence of distributed metadata,
649
we store all the top-level metadata in a single range (the first range). These
650
top-level metadata keys are known as *meta1* keys, and are prefixed such that
651
they sort to the beginning of the key space. Given the metadata size of 256
652
bytes given above, a single 64M range would support 64M/256B = 2\^18 ranges,
653
which gives a total storage of 64M \* 2\^18 = 16.7T. To support the 1P quoted
654
above, we need two levels of indirection, where the first level addresses the
655
second, and the second addresses user data. With two levels of indirection, we
656
can address 2\^(18 + 18) = 2\^36 ranges; each range addresses 2\^26 B, and
657
altogether we address 2\^(36+26) B = 2\^62 B = 4E of user data.
658
659
For a given user-addressable `key1`, the associated *meta1* record is found
660
at the successor key to `key1` in the *meta1* space. Since the *meta1* space
661
is sparse, the successor key is defined as the next key which is present. The
662
*meta1* record identifies the range containing the *meta2* record, which is
663
found using the same process. The *meta2* record identifies the range
664
containing `key1`, which is again found the same way (see examples below).
665
666
Concretely, metadata keys are prefixed by `\0\0meta{1,2}`; the two null
667
characters provide for the desired sorting behaviour. Thus, `key1`'s
668
*meta1* record will reside at the successor key to `\0\0\meta1<key1>`.
669
671
the RocksDB iterator only supports a Seek() interface which acts as a
672
Ceil(). Using the start key of the range would cause Seek() to find the
673
key *after* the meta indexing record we’re looking for, which would
674
result in having to back the iterator up, an option which is both less
675
efficient and not available in all cases.
676
677
The following example shows the directory structure for a map with
678
three ranges worth of data. Ellipses indicate additional key/value pairs to
679
fill an entire range of data. Except for the fact that splitting ranges
680
requires updates to the range metadata with knowledge of the metadata layout,
681
the range metadata itself requires no special treatment or bootstrapping.
682
683
**Range 0** (located on servers `dcrama1:8000`, `dcrama2:8000`,
684
`dcrama3:8000`)
685
686
- `\0\0meta1\xff`: `dcrama1:8000`, `dcrama2:8000`, `dcrama3:8000`
687
- `\0\0meta2<lastkey0>`: `dcrama1:8000`, `dcrama2:8000`, `dcrama3:8000`
688
- `\0\0meta2<lastkey1>`: `dcrama4:8000`, `dcrama5:8000`, `dcrama6:8000`
689
- `\0\0meta2\xff`: `dcrama7:8000`, `dcrama8:8000`, `dcrama9:8000`
690
- ...
691
- `<lastkey0>`: `<lastvalue0>`
692
693
**Range 1** (located on servers `dcrama4:8000`, `dcrama5:8000`,
694
`dcrama6:8000`)
695
696
- ...
697
- `<lastkey1>`: `<lastvalue1>`
698
699
**Range 2** (located on servers `dcrama7:8000`, `dcrama8:8000`,
700
`dcrama9:8000`)
701
702
- ...
703
- `<lastkey2>`: `<lastvalue2>`
704
705
Consider a simpler example of a map containing less than a single
706
range of data. In this case, all range metadata and all data are
707
located in the same range:
708
709
**Range 0** (located on servers `dcrama1:8000`, `dcrama2:8000`,
710
`dcrama3:8000`)*
711
712
- `\0\0meta1\xff`: `dcrama1:8000`, `dcrama2:8000`, `dcrama3:8000`
713
- `\0\0meta2\xff`: `dcrama1:8000`, `dcrama2:8000`, `dcrama3:8000`
714
- `<key0>`: `<value0>`
715
- `...`
716
717
Finally, a map large enough to need both levels of indirection would
718
look like (note that instead of showing range replicas, this
719
example is simplified to just show range indexes):
720
721
**Range 0**
722
723
- `\0\0meta1<lastkeyN-1>`: Range 0
724
- `\0\0meta1\xff`: Range 1
725
- `\0\0meta2<lastkey1>`: Range 1
726
- `\0\0meta2<lastkey2>`: Range 2
727
- `\0\0meta2<lastkey3>`: Range 3
728
- ...
729
- `\0\0meta2<lastkeyN-1>`: Range 262143
730
731
**Range 1**
732
733
- `\0\0meta2<lastkeyN>`: Range 262144
734
- `\0\0meta2<lastkeyN+1>`: Range 262145
735
- ...
736
- `\0\0meta2\xff`: Range 500,000
737
- ...
738
- `<lastkey1>`: `<lastvalue1>`
739
740
**Range 2**
741
742
- ...
743
- `<lastkey2>`: `<lastvalue2>`
744
745
**Range 3**
746
747
- ...
748
- `<lastkey3>`: `<lastvalue3>`
749
750
**Range 262144**
751
752
- ...
753
- `<lastkeyN>`: `<lastvalueN>`
754
755
**Range 262145**
756
757
- ...
758
- `<lastkeyN+1>`: `<lastvalueN+1>`
759
760
Note that the choice of range `262144` is just an approximation. The
761
actual number of ranges addressable via a single metadata range is
762
dependent on the size of the keys. If efforts are made to keep key sizes
763
small, the total number of addressable ranges would increase and vice
764
versa.
765
766
From the examples above it’s clear that key location lookups require at
767
most three reads to get the value for `<key>`:
768
769
1. lower bound of `\0\0meta1<key>`
770
2. lower bound of `\0\0meta2<key>`,
771
3. `<key>`.
772
773
For small maps, the entire lookup is satisfied in a single RPC to Range 0. Maps
774
containing less than 16T of data would require two lookups. Clients cache both
775
levels of range metadata, and we expect that data locality for individual
776
clients will be high. Clients may end up with stale cache entries. If on a
777
lookup, the range consulted does not match the client’s expectations, the
778
client evicts the stale entries and possibly does a new lookup.
779
782
Each range is configured to consist of three or more replicas, as specified by
783
their ZoneConfig. The replicas in a range maintain their own instance of a
784
distributed consensus algorithm. We use the [*Raft consensus algorithm*](https://raftconsensus.github.io)
787
[ePaxos](https://www.cs.cmu.edu/~dga/papers/epaxos-sosp2013.pdf) has
788
promising performance characteristics for WAN-distributed replicas, but
789
it does not guarantee a consistent ordering between replicas.
790
791
Raft elects a relatively long-lived leader which must be involved to
793
replicated. In the absence of heartbeats, followers become candidates
794
after randomized election timeouts and proceed to hold new leader
795
elections. Cockroach weights random timeouts such that the replicas with
796
shorter round trip times to peers are more likely to hold elections
797
first (not implemented yet). Only the Raft leader may propose commands;
798
followers will simply relay commands to the last known leader.
800
Our Raft implementation was developed together with CoreOS, but adds an extra
801
layer of optimization to account for the fact that a single Node may have
802
millions of consensus groups (one for each Range). Areas of optimization
803
are chiefly coalesced heartbeats (so that the number of nodes dictates the
804
number of heartbeats as opposed to the much larger number of ranges) and
805
batch processing of requests.
806
Future optimizations may include two-phase elections and quiescent ranges
807
(i.e. stopping traffic completely for inactive ranges).
808
809
# Range Leadership (Leader Leases)
810
811
As outlined in the Raft section, the replicas of a Range are organized as a
812
Raft group and execute commands from their shared commit log. Going through
813
Raft is an expensive operation though, and there are tasks which should only be
814
carried out by a single replica at a time (as opposed to all of them).
815
816
For these reasons, Cockroach introduces the concept of **Range Leadership**:
817
This is a lease held for a slice of (database, i.e. hybrid logical) time and is
818
established by committing a special log entry through Raft containing the
819
interval the leadership is going to be active on, along with the Node:RaftID
820
combination that uniquely describes the requesting replica. Reads and writes
821
must generally be addressed to the replica holding the lease; if none does, any
822
replica may be addressed, causing it to try to obtain the lease synchronously.
823
Requests received by a non-leader (for the HLC timestamp specified in the
824
request's header) fail with an error pointing at the replica's last known
825
leader. These requests are retried transparently with the updated leader by the
826
gateway node and never reach the client.
827
828
The replica holding the lease is in charge or involved in handling
829
Range-specific maintenance tasks such as
830
831
* gossiping the sentinel and/or first range information
832
* splitting, merging and rebalancing
833
834
and, very importantly, may satisfy reads locally, without incurring the
835
overhead of going through Raft.
836
837
Since reads bypass Raft, a new lease holder will, among other things, ascertain
838
that its timestamp cache does not report timestamps smaller than the previous
839
lease holder's (so that it's compatible with reads which may have occurred on
840
the former leader). This is accomplished by setting the low water mark of the
841
timestamp cache to the expiration of the previous lease plus the maximum clock
842
offset.
843
844
## Relationship to Raft leadership
845
846
Range leadership is completely separate from Raft leadership, and so without
847
further efforts, Raft and Range leadership may not be represented by the same
848
replica most of the time. This is convenient semantically since it decouples
849
these two types of leadership and allows the use of Raft as a "black box", but
850
for reasons of performance, it is desirable to have both on the same replica.
851
Otherwise, sending a command through Raft always incurs the overhead of being
852
proposed to the Range leader's Raft instance first, which must relay it to the
853
Raft leader, which finally commits it into the log and updates its followers,
854
including the Range leader. This yields correct results but wastes several
855
round-trip delays, and so we will make sure that in the vast majority of cases
856
Range and Raft leadership coincide. A fairly easy method for achieving this is
857
to have each new lease period (extension or new) be accompanied by a
858
stipulation to the lease holder's replica to start Raft elections (unless it's
859
already leading), though some care should be taken that Range leadership is
860
relatively stable and long-lived to avoid a large number of Raft leadership
861
transitions.
862
863
# Splitting / Merging Ranges
864
865
RoachNodes split or merge ranges based on whether they exceed maximum or
866
minimum thresholds for capacity or load. Ranges exceeding maximums for
867
either capacity or load are split; ranges below minimums for *both*
868
capacity and load are merged.
869
870
Ranges maintain the same accounting statistics as accounting key
871
prefixes. These boil down to a time series of data points with minute
872
granularity. Everything from number of bytes to read/write queue sizes.
873
Arbitrary distillations of the accounting stats can be determined as the
874
basis for splitting / merging. Two sensical metrics for use with
875
split/merge are range size in bytes and IOps. A good metric for
876
rebalancing a replica from one node to another would be total read/write
877
queue wait times. These metrics are gossipped, with each range / node
878
passing along relevant metrics if they’re in the bottom or top of the
879
range it’s aware of.
880
881
A range finding itself exceeding either capacity or load threshold
882
splits. To this end, the range leader computes an appropriate split key
883
candidate and issues the split through Raft. In contrast to splitting,
884
merging requires a range to be below the minimum threshold for both
885
capacity *and* load. A range being merged chooses the smaller of the
886
ranges immediately preceding and succeeding it.
887
888
Splitting, merging, rebalancing and recovering all follow the same basic
889
algorithm for moving data between roach nodes. New target replicas are
890
created and added to the replica set of source range. Then each new
891
replica is brought up to date by either replaying the log in full or
892
copying a snapshot of the source replica data and then replaying the log
893
from the timestamp of the snapshot to catch up fully. Once the new
894
replicas are fully up to date, the range metadata is updated and old,
895
source replica(s) deleted if applicable.
896
897
**Coordinator** (leader replica)
898
899
```
900
if splitting
901
SplitRange(split_key): splits happen locally on range replicas and
902
only after being completed locally, are moved to new target replicas.
903
else if merging
904
Choose new replicas on same servers as target range replicas;
905
add to replica set.
906
else if rebalancing || recovering
907
Choose new replica(s) on least loaded servers; add to replica set.
908
```
909
910
**New Replica**
911
912
*Bring replica up to date:*
913
914
```
915
if all info can be read from replicated log
916
copy replicated log
917
else
918
snapshot source replica
919
send successive ReadRange requests to source replica
920
referencing snapshot
921
922
if merging
923
combine ranges on all replicas
924
else if rebalancing || recovering
925
remove old range replica(s)
926
```
927
928
RoachNodes split ranges when the total data in a range exceeds a
929
configurable maximum threshold. Similarly, ranges are merged when the
930
total data falls below a configurable minimum threshold.
931
932
**TBD: flesh this out**: Especially for merges (but also rebalancing) we have a
933
range disappearing from the local node; that range needs to disappear
934
gracefully, with a smooth handoff of operation to the new owner of its data.
935
936
Ranges are rebalanced if a node determines its load or capacity is one
937
of the worst in the cluster based on gossipped load stats. A node with
938
spare capacity is chosen in the same datacenter and a special-case split
939
is done which simply duplicates the data 1:1 and resets the range
940
configuration metadata.
941
942
# Range-Spanning Binary Tree
943
944
A crucial enhancement to the organization of range metadata is to
945
augment the bi-level range metadata lookup with a minimum spanning tree,
946
implemented as a left-leaning red-black tree over all ranges in the map.
947
This tree structure allows the system to start at any key prefix and
948
efficiently traverse an arbitrary key range with minimal RPC traffic,
949
minimal fan-in and fan-out, and with bounded time complexity equal to
950
`2*log N` steps, where `N` is the total number of ranges in the system.
951
952
Unlike the range metadata rows prefixed with `\0\0meta[1|2]`, the
953
metadata for the range-spanning tree (e.g. parent range and left / right
954
child ranges) is stored directly at the ranges as non-map metadata. The
955
metadata for each node of the tree (e.g. links to parent range, left
956
child range, and right child range) is stored with the range metadata.
957
In effect, the tree metadata is stored implicitly. In order to traverse
958
the tree, for example, you’d need to query each range in turn for its
959
metadata.
960
961
Any time a range is split or merged, both the bi-level range lookup
962
metadata and the per-range binary tree metadata are updated as part of
963
the same distributed transaction. The total number of nodes involved in
964
the update is bounded by 2 + log N (i.e. 2 updates for meta1 and
965
meta2, and up to log N updates to balance the range-spanning tree).
966
The range corresponding to the root node of the tree is stored in
967
*\0tree_root*.
968
969
As an example, consider the following set of nine ranges and their
970
associated range-spanning tree:
971
972
R0: `aa - cc`, R1: `*cc - lll`, R2: `*lll - llr`, R3: `*llr - nn`, R4: `*nn - rr`, R5: `*rr - ssss`, R6: `*ssss - sst`, R7: `*sst - vvv`, R8: `*vvv - zzzz`.
973
974
975
976
The range-spanning tree has many beneficial uses in Cockroach. It
977
provides a ready made solution to scheduling mappers and sorting /
978
reducing during map-reduce operations. It also provides a mechanism
979
for visiting every Raft replica range which comprises a logical key
980
range. This is used to periodically find the oldest extant write
981
intent over the entire system.
982
983
The range-spanning tree provides a convenient mechanism for planning
984
and executing parallel queries. These provide the basis for
985
[Dremel](http://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/36632.pdf)-like
986
query execution trees and it’s easy to imagine supporting a subset of
987
SQL or even javascript-based user functions for complex data analysis
988
tasks.
989
990
991
992
# Node Allocation (via Gossip)
993
994
New nodes must be allocated when a range is split. Instead of requiring
995
every RoachNode to know about the status of all or even a large number
996
of peer nodes --or-- alternatively requiring a specialized curator or
997
master with sufficiently global knowledge, we use a gossip protocol to
998
efficiently communicate only interesting information between all of the
999
nodes in the cluster. What’s interesting information? One example would
1000
be whether a particular node has a lot of spare capacity. Each node,
1001
when gossiping, compares each topic of gossip to its own state. If its
1002
own state is somehow “more interesting” than the least interesting item
1003
in the topic it’s seen recently, it includes its own state as part of
1004
the next gossip session with a peer node. In this way, a node with
1005
capacity sufficiently in excess of the mean quickly becomes discovered
1006
by the entire cluster. To avoid piling onto outliers, nodes from the
1007
high capacity set are selected at random for allocation.
1008
1009
The gossip protocol itself contains two primary components:
1010
1011
- **Peer Selection**: each node maintains up to N peers with which it
1012
regularly communicates. It selects peers with an eye towards
1013
maximizing fanout. A peer node which itself communicates with an
1014
array of otherwise unknown nodes will be selected over one which
1015
communicates with a set containing significant overlap. Each time
1016
gossip is initiated, each nodes’ set of peers is exchanged. Each
1017
node is then free to incorporate the other’s peers as it sees fit.
1018
To avoid any node suffering from excess incoming requests, a node
1019
may refuse to answer a gossip exchange. Each node is biased
1020
towards answering requests from nodes without significant overlap
1021
and refusing requests otherwise.
1022
1023
Peers are efficiently selected using a heuristic as described in
1024
[Agarwal & Trachtenberg (2006)](https://drive.google.com/file/d/0B9GCVTp_FHJISmFRTThkOEZSM1U/edit?usp=sharing).
1025
1026
**TBD**: how to avoid partitions? Need to work out a simulation of
1027
the protocol to tune the behavior and see empirically how well it
1028
works.
1029
1030
- **Gossip Selection**: what to communicate. Gossip is divided into
1031
topics. Load characteristics (capacity per disk, cpu load, and
1032
state [e.g. draining, ok, failure]) are used to drive node
1033
allocation. Range statistics (range read/write load, missing
1034
replicas, unavailable ranges) and network topology (inter-rack
1035
bandwidth/latency, inter-datacenter bandwidth/latency, subnet
1036
outages) are used for determining when to split ranges, when to
1037
recover replicas vs. wait for network connectivity, and for
1038
debugging / sysops. In all cases, a set of minimums and a set of
1039
maximums is propagated; each node applies its own view of the
1040
world to augment the values. Each minimum and maximum value is
1041
tagged with the reporting node and other accompanying contextual
1042
information. Each topic of gossip has its own protobuf to hold the
1043
structured data. The number of items of gossip in each topic is
1044
limited by a configurable bound.
1045
1046
For efficiency, nodes assign each new item of gossip a sequence
1047
number and keep track of the highest sequence number each peer
1048
node has seen. Each round of gossip communicates only the delta
1049
containing new items.
1050
1051
# Node Accounting
1052
1053
The gossip protocol discussed in the previous section is useful to
1054
quickly communicate fragments of important information in a
1055
decentralized manner. However, complete accounting for each node is also
1056
stored to a central location, available to any dashboard process. This
1057
is done using the map itself. Each node periodically writes its state to
1058
the map with keys prefixed by `\0node`, similar to the first level of
1059
range metadata, but with an ‘`node`’ suffix. Each value is a protobuf
1060
containing the full complement of node statistics--everything
1061
communicated normally via the gossip protocol plus other useful, but
1062
non-critical data.
1063
1064
The range containing the first key in the node accounting table is
1065
responsible for gossiping the total count of nodes. This total count is
1066
used by the gossip network to most efficiently organize itself. In
1067
particular, the maximum number of hops for gossipped information to take
1068
before reaching a node is given by `ceil(log(node count) / log(max
1069
fanout)) + 1`.
1070
1071
# Key-prefix Accounting, Zones & Permissions
1072
1073
Arbitrarily fine-grained accounting and permissions are specified via
1074
key prefixes. Key prefixes can overlap, as is necessary for capturing
1075
hierarchical relationships. For illustrative purposes, let’s say keys
1076
specifying rows in a set of databases have the following format:
1077
1078
`<db>:<table>:<primary-key>[:<secondary-key>]`
1079
1080
In this case, we might collect accounting or specify permissions with
1081
key prefixes:
1082
1083
`db1`, `db1:user`, `db1:order`,
1084
1085
Accounting is kept for the entire map by default.
1086
1087
## Accounting
1088
to keep accounting for a range defined by a key prefix, an entry is created in
1089
the accounting system table. The format of accounting table keys is:
1090
1091
`\0acct<key-prefix>`
1092
1093
In practice, we assume each RoachNode capable of caching the
1094
entire accounting table as it is likely to be relatively small.
1095
1096
Accounting is kept for key prefix ranges with eventual consistency for
1097
efficiency. There are two types of values which comprise accounting:
1098
counts and occurrences, for lack of better terms. Counts describe
1099
system state, such as the total number of bytes, rows,
1100
etc. Occurrences include transient performance and load metrics. Both
1101
types of accounting are captured as time series with minute
1102
granularity. The length of time accounting metrics are kept is
1103
configurable. Below are examples of each type of accounting value.
1104
1105
**System State Counters/Performance**
1106
1107
- Count of items (e.g. rows)
1108
- Total bytes
1109
- Total key bytes
1110
- Total value length
1111
- Queued message count
1112
- Queued message total bytes
1113
- Count of values \< 16B
1114
- Count of values \< 64B
1115
- Count of values \< 256B
1116
- Count of values \< 1K
1117
- Count of values \< 4K
1118
- Count of values \< 16K
1119
- Count of values \< 64K
1120
- Count of values \< 256K
1121
- Count of values \< 1M
1122
- Count of values \> 1M
1123
- Total bytes of accounting
1124
1125
1126
**Load Occurences**
1127
1128
Get op count
1129
Get total MB
1130
Put op count
1131
Put total MB
1132
Delete op count
1133
Delete total MB
1134
Delete range op count
1135
Delete range total MB
1136
Scan op count
1137
Scan op MB
1138
Split count
1139
Merge count
1140
1141
Because accounting information is kept as time series and over many
1142
possible metrics of interest, the data can become numerous. Accounting
1143
data are stored in the map near the key prefix described, in order to
1144
distribute load (for both aggregation and storage).
1145
1146
Accounting keys for system state have the form:
1147
`<key-prefix>|acctd<metric-name>*`. Notice the leading ‘pipe’
1148
character. It’s meant to sort the root level account AFTER any other
1149
system tables. They must increment the same underlying values as they
1150
are permanent counts, and not transient activity. Logic at the
1151
RoachNode takes care of snapshotting the value into an appropriately
1152
suffixed (e.g. with timestamp hour) multi-value time series entry.
1153
1154
Keys for perf/load metrics:
1155
`<key-prefix>acctd<metric-name><hourly-timestamp>`.
1156
1157
`<hourly-timestamp>`-suffixed accounting entries are multi-valued,
1158
containing a varint64 entry for each minute with activity during the
1159
specified hour.
1160
1161
To efficiently keep accounting over large key ranges, the task of
1162
aggregation must be distributed. If activity occurs within the same
1163
range as the key prefix for accounting, the updates are made as part
1164
of the consensus write. If the ranges differ, then a message is sent
1165
to the parent range to increment the accounting. If upon receiving the
1166
message, the parent range also does not include the key prefix, it in
1167
turn forwards it to its parent or left child in the balanced binary
1168
tree which is maintained to describe the range hierarchy. This limits
1169
the number of messages before an update is visible at the root to `2*log N`,
1170
where `N` is the number of ranges in the key prefix.
1171
1172
## Zones
1173
zones are stored in the map with keys prefixed by
1174
`\0zone` followed by the key prefix to which the zone
1175
configuration applies. Zone values specify a protobuf containing
1176
the datacenters from which replicas for ranges which fall under
1177
the zone must be chosen.
1178
1179
Please see [storage/config/config.proto](https://github.com/cockroachdb/cockroach/blob/master/storage/config/config.proto) for up-to-date data structures used, the best entry point being `message ZoneConfig`.
1180
1181
If zones are modified in situ, each RoachNode verifies the
1182
existing zones for its ranges against the zone configuration. If
1183
it discovers differences, it reconfigures ranges in the same way
1184
that it rebalances away from busy nodes, via special-case 1:1
1185
split to a duplicate range comprising the new configuration.
1186
1188
permissions are stored in the map with keys prefixed by *\0perm* followed by
1189
the key prefix and user to which the specified permissions apply. The format of
1190
permissions keys is:
1191
1192
`\0perm<key-prefix><user>`
1193
1195
please see [storage/config/config.proto](https://github.com/cockroachdb/cockroach/blob/master/storage/config/config.proto) for up-to-date data structures used, the best entry point being `message PermConfig`.
1196
1197
A default system root permission is assumed for the entire map
1198
with an empty key prefix and read/write as true.
1199
1200
When determining whether or not to allow a read or a write a key
1201
value (e.g. `db1:user:1` for user `spencer`), a RoachNode would
1202
read the following permissions values:
1203
1204
```
1205
\0perm<db1:user:1>spencer
1206
\0perm<db1:user>spencer
1207
\0perm<db1>spencer
1208
\0perm<>spencer
1209
```
1210
1211
If any prefix in the hierarchy provides the required permission,
1212
the request is satisfied; otherwise, the request returns an
1213
error.
1214
1215
The priority for a user permission is used to order requests at
1216
Raft consensus ranges and for choosing an initial priority for
1217
distributed transactions. When scheduling operations at the Raft
1218
consensus range, all outstanding requests are ordered by key
1219
prefix and each assigned priorities according to key, user and
1220
arrival time. The next request is chosen probabilistically using
1221
priorities to weight the choice. Each key can have multiple
1222
priorities as they’re hierarchical (e.g. for /user/key, one
1223
priority for root ‘/’, and one for ‘/user/key’). The most general
1224
priority is used first. If two keys share the most general, then
1225
they’re compared with the next most general if applicable, and so on.
1226
1227
# Key-Value API
1228
1229
see the protobufs in [proto/](https://github.com/cockroachdb/cockroach/blob/master/proto),
1230
in particular [proto/api.proto](https://github.com/cockroachdb/cockroach/blob/master/proto/api.proto) and the comments within.
1231
1232
# Structured Data API
1233
1234
A preliminary design can be found in the [Go source documentation](http://godoc.org/github.com/cockroachdb/cockroach/structured).