Zebra logotype

codecov License


Zebra is the Zcash Foundation's independent, consensus-compatible implementation of the Zcash protocol, currently under development. Please join us on Discord if you'd like to find out more or get involved!

Alpha Releases

Every few weeks, we release a new Zebra alpha release.

The goals of the alpha release series are to:

  • participate in the Zcash network,
  • replicate the Zcash chain state,
  • implement the Zcash proof of work consensus rules, and
  • sync on Mainnet under excellent network conditions.

Currently, Zebra does not validate all the Zcash consensus rules. It may be unreliable on Testnet, and under less-than-perfect network conditions. See our current features and roadmap for details.

Getting Started

Building zebrad requires Rust, libclang, and a C++ compiler.

Detailed Build and Run Instructions

  1. Install cargo and rustc.
    • Using rustup installs the stable Rust toolchain, which zebrad targets.
  2. Install Zebra's build dependencies:
    • libclang: the libclang, libclang-dev, llvm, or llvm-dev packages, depending on your package manager
    • clang or another C++ compiler: g++, Xcode, or MSVC
  3. Run cargo install --locked --git https://github.com/ZcashFoundation/zebra --tag v1.0.0-alpha.6 zebrad
  4. Run zebrad start

If you're interested in testing out zebrad please feel free, but keep in mind that there is a lot of key functionality still missing.

Build Troubleshooting

If you're having trouble with:

  • dependencies:
    • install both libclang and clang - they are usually different packages
    • use cargo install without --locked to build with the latest versions of each dependency
  • libclang: check out the clang-sys documentation
  • g++ or MSVC++: try using clang or Xcode instead
  • rustc: use rustc 1.48 or later
    • Zebra does not have a minimum supported Rust version (MSRV) policy yet

System Requirements

We usually build zebrad on systems with:

  • 2+ CPU cores
  • 7+ GB RAM
  • 14+ GB of disk space

On many-core machines (like, 32-core) the build is very fast; on 2-core machines it's less fast.

We continuously test that our builds and tests pass on:

  • Windows Server 2019
  • macOS Big Sur 11.0
  • Ubuntu 18.04 / the latest LTS
  • Debian Buster

We usually run zebrad on systems with:

  • 4+ CPU cores
  • 16+ GB RAM
  • 50GB+ available disk space for finalized state
  • 100+ Mbps network connections

zebrad might build and run fine on smaller and slower systems - we haven't tested its exact limits yet.

Network Usage

zebrad's typical network usage is:

  • initial sync: 30 GB download
  • ongoing updates: 10-50 MB upload and download per day, depending on peer requests

The major constraint we've found on zebrad performance is the network weather, especially the ability to make good connections to other Zcash network peers.

Current Features


  • synchronize the chain from peers
  • download gossipped blocks from peers
  • answer inbound peer requests for hashes, headers, and blocks


  • persist block, transaction, UTXO, and nullifier indexes
  • handle chain reorganizations

Proof of Work:

  • validate equihash, block difficulty threshold, and difficulty adjustment
  • validate transaction merkle roots

Validating proof of work increases the cost of creating a consensus split between zebrad and zcashd.

This release also implements some other Zcash consensus rules, to check that Zebra's validation architecture supports future work on a full validating node:

  • block and transaction structure
  • checkpoint-based verification up to Canopy
  • transaction validation (incomplete)
  • transaction cryptography (incomplete)
  • transaction scripts (incomplete)
  • batch verification (incomplete)


Zebra primarily depends on pure Rust crates, and some Rust/C++ crates:

Known Issues

There are a few bugs in Zebra that we're still working on fixing:

Future Work

In 2021, we intend to finish validation, add RPC support, and add wallet integration. This phased approach allows us to test Zebra's independent implementation of the consensus rules, before asking users to entrust it with their funds.


  • full consensus rule validation
  • transaction mempool
  • wallet functionality
  • RPC functionality

Performance and Reliability:

  • reliable syncing on Testnet
  • reliable syncing under poor network conditions
  • batch verification
  • performance tuning


The Zebra website contains user documentation, such as how to run or configure Zebra, set up metrics integrations, etc., as well as developer documentation, such as design documents. We also render API documentation for the external API of our crates, as well as internal documentation for private APIs.


Unlike zcashd, which originated as a Bitcoin Core fork and inherited its monolithic architecture, Zebra has a modular, library-first design, with the intent that each component can be independently reused outside of the zebrad full node. For instance, the zebra-network crate containing the network stack can also be used to implement anonymous transaction relay, network crawlers, or other functionality, without requiring a full node.

At a high level, the fullnode functionality required by zebrad is factored into several components:

  • zebra-chain, providing definitions of core data structures for Zcash, such as blocks, transactions, addresses, etc., and related functionality. It also contains the implementation of the consensus-critical serialization formats used in Zcash. The data structures in zebra-chain are defined to enforce structural validity by making invalid states unrepresentable. For instance, the Transaction enum has variants for each transaction version, and it's impossible to construct a transaction with, e.g., spend or output descriptions but no binding signature, or, e.g., a version 2 (Sprout) transaction with Sapling proofs. Currently, zebra-chain is oriented towards verifying transactions, but will be extended to support creating them in the future.

  • zebra-network, providing an asynchronous, multithreaded implementation of the Zcash network protocol inherited from Bitcoin. In contrast to zcashd, each peer connection has a separate state machine, and the crate translates the external network protocol into a stateless, request/response-oriented protocol for internal use. The crate provides two interfaces:

    • an auto-managed connection pool that load-balances local node requests over available peers, and sends peer requests to a local inbound service, and
    • a connect_isolated method that produces a peer connection completely isolated from all other node state. This can be used, for instance, to safely relay data over Tor, without revealing distinguishing information.
  • zebra-script provides script validation. Currently, this is implemented by linking to the C++ script verification code from zcashd, but in the future we may implement a pure-Rust script implementation.

  • zebra-consensus performs semantic validation of blocks and transactions: all consensus rules that can be checked independently of the chain state, such as verification of signatures, proofs, and scripts. Internally, the library uses tower-batch to perform automatic, transparent batch processing of contemporaneous verification requests.

  • zebra-state is responsible for storing, updating, and querying the chain state. The state service is responsible for contextual verification: all consensus rules that check whether a new block is a valid extension of an existing chain, such as updating the nullifier set or checking that transaction inputs remain unspent.

  • zebrad contains the full node, which connects these components together and implements logic to handle inbound requests from peers and the chain sync process.

  • zebra-rpc and zebra-client will eventually contain the RPC and wallet functionality, but as mentioned above, our goal is to implement replication of chain state first before asking users to entrust Zebra with their funds.

All of these components can be reused as independent libraries, and all communication between stateful components is handled internally by internal asynchronous RPC abstraction ("microservices in one process").


Zebra has a responsible disclosure policy, which we encourage security researchers to follow.


Zebra is distributed under the terms of both the MIT license and the Apache License (Version 2.0).


User Documentation

This section contains details on how to install, run, and instrument Zebra.

Installing Zebra

Zebra is still under development, so there is no supported packaging or install mechanism. To run Zebra, check out the git repository:

git clone https://github.com/ZcashFoundation/zebra

and then run

cargo build

Be aware that Zebra is still in an extremely early stage of development.

Running Zebra

zebrad generate generates a default config. These defaults will be used if no config is present, so it's not necessary to generate a config. However, having a config file with the default fields is a useful starting point for changing the config.

The configuration format is the TOML encoding of the internal config structure, and documentation for all of the config options can be found here.

  • zebrad start starts a full node.

Return Codes

  • 0: Application exited successfully
  • 1: Application exited unsuccessfully
  • 2: Application crashed
  • zebrad may also return platform-dependent codes.

Tracing Zebra

Zebra supports dynamic tracing, configured using the config's TracingSection and (optionally) an HTTP RPC endpoint.

If the endpoint_addr is specified, zebrad will open an HTTP endpoint allowing dynamic runtime configuration of the tracing filter. For instance, if the config had endpoint_addr = '', then

  • curl -X GET localhost:3000/filter retrieves the current filter string;
  • curl -X POST localhost:3000/filter -d "zebrad=trace" sets the current filter string.

See the filter documentation for more details.

Zebra also has support for:

Zebra Metrics

Zebra has support for Prometheus, configured using the MetricsSection.

This requires supporting infrastructure to collect and visualize metrics, for example:

  1. Install Prometheus and Grafana via Docker
# create a storage volume for grafana (once)
sudo docker volume create grafana-storage
# create a storage volume for prometheus (once)
sudo docker volume create prometheus-storage

# run prometheus with the included config
sudo docker run --detach --network host --volume prometheus-storage:/prometheus --volume /path/to/zebra/prometheus.yaml:/etc/prometheus/prometheus.yml  prom/prometheus

# run grafana
sudo docker run --detach --network host --env GF_SERVER_HTTP_PORT=3030 --env GF_SERVER_HTTP_ADDR=localhost --volume grafana-storage:/var/lib/grafana grafana/grafana

Now the grafana dashboard is available at http://localhost:3030 ; the default username and password is admin/admin. Prometheus scrapes Zebra on localhost:9999, and provides the results on locahost:9090.

  1. Configure Grafana with a Prometheus HTTP Data Source, using Zebra's metrics.endpoint_addr.

In zebrad.toml:

endpoint_addr = ""

In the grafana dashboard:

  1. Create a new Prometheus Data Source Prometheus-Zebra
  2. Enter the HTTP URL:
  3. Save the configuration

Now you can add the grafana dashboards from zebra/grafana, or create your own.

Developer Documentation

This section contains the contribution guide and design documentation. It does not contain API documentation, which is generated using Rustdoc:


Running and Debugging

See the user documentation for details on how to build, run, and instrument Zebra.

Bug Reports

File an issue on the issue tracker using the bug report template.

Pull Requests

PRs are welcome for small and large changes, but please don't make large PRs without coordinating with us via the issue tracker or Discord. This helps increase development coordination and makes PRs easier to merge.

Check out the help wanted or good first issue labels if you're looking for a place to get started!

Coverage Reports

Zebra's CI currently generates coverage reports for every PR with rust's new source based coverage feature. The coverage reports are generated by the coverage.yml file.

These reports are then saved as html and zipped up into a github action's artifact. These artifacts can be accessed on the checks tab of any PR, next to the "re-run jobs" button on the Coverage (+nightly) CI job's tab example.

To access a report download and extract the zip artifact then open the top level index.html.

Zebra RFCs

Significant changes to the Zebra codebase are planned using Zebra RFCs. These allow structured discussion about a proposed change and provide a record of the planned design.

To make a Zebra RFC:

  1. Choose a short feature name like my-feature.

  2. Copy the book/src/dev/rfcs/0000-template.md file to book/src/dev/rfcs/drafts/xxxx-my-feature.md.

  3. Edit the template header to add the feature name and the date, but leave the other fields blank for now.

  4. Write the design! The template has a suggested list of sections that are a useful guide.

  5. Create an design PR using the RFC template.

  6. After creating an RFC PR, update the RFC header and the PR description with the PR number.

  7. Make changes to the RFC in collaboration with the Zebra team.

  8. When the RFC is merged, take the next available RFC number (not conflicting with any existing RFCs or design PRs) and name the RFC file accordingly, e.g., 0027-my-feature.md for number 27. Make sure that book/src/SUMMARY.md links to the numbered RFC.

  9. After the RFC is accepted, create an issue for the implementation of the design, and update the RFC header and PR description with the implementation issue number.

Design Overview

This document sketches the future design for Zebra.


The following are general desiderata for Zebra:

  • [George's list..]

  • As much as reasonably possible, it and its dependencies should be implemented in Rust. While it may not make sense to require this in every case (for instance, it probably doesn't make sense to rewrite libsecp256k1 in Rust, instead of using the same upstream library as Bitcoin), we should generally aim for it.

  • As much as reasonably possible, Zebra should minimize trust in required dependencies. Note that "minimize number of dependencies" is usually a proxy for this desideratum, but is not exactly the same: for instance, a collection of crates like the tokio crates are all developed together and have one trust boundary.

  • Zebra should be well-factored internally into a collection of component libraries which can be used by other applications to perform Zcash-related tasks. Implementation details of each component should not leak into all other components.

  • Zebra should checkpoint on Canopy activation and drop all Sprout-related functionality not required post-Canopy.


  • Zebra keeps a copy of the chain state, so it isn't intended for lightweight applications like light wallets. Those applications should use a light client protocol.

Internal Structure

The following is a list of internal component libraries (crates), and a description of functional responsibility.


Internal Dependencies

None: these are the core data structure definitions.

Responsible for

  • definitions of commonly used data structures, e.g.,

    • Block,
    • Transaction,
    • Address,
    • KeyPair...
  • parsing bytes into these data structures

  • definitions of core traits, e.g.,

    • ZcashSerialize and ZcashDeserialize, which perform consensus-critical serialization logic.

Exported types

  • [...]


Internal Dependencies

  • zebra-chain

Responsible for

  • definition of a well structured, internal request/response protocol
  • provides an abstraction for "this node" and "the network" using the internal protocol
  • dynamic, backpressure-driven peer set management
  • per-peer state machine that translates the internal protocol to the Bitcoin/Zcash protocol
  • tokio codec for Bitcoin/Zcash message encoding.

Exported types

  • Request, an enum representing all possible requests in the internal protocol;
  • Response, an enum representing all possible responses in the internal protocol;
  • AddressBook, a data structure for storing peer addresses;
  • Config, a configuration object for all networking-related parameters;
  • init<S: Service>(Config, S) -> (impl Service, Arc<Mutex<AddressBook>>), the main entry-point.

The init entrypoint constructs a dynamically-sized pool of peers sending inbound requests to the provided S: tower::Service representing "this node", and returns a Service that can be used to send requests to "the network", together with an AddressBook updated with liveness information from the peer pool. The AddressBook can be used to respond to inbound requests for peers.

All peerset management (finding new peers, creating new outbound connections, etc) is completely encapsulated, as is responsibility for routing outbound requests to appropriate peers.


Internal Dependencies

  • zebra-chain for data structure definitions.

Responsible for

  • block storage API
    • operates on parsed block structs
      • these structs can be converted from and into raw bytes
    • primarily aimed at network replication, not at processing
    • can be used to rebuild the database below
  • maintaining a database of tx, address, etc data
    • this database can be blown away and rebuilt from the blocks, which are otherwise unused.
    • threadsafe, typed lookup API that completely encapsulates the database logic
    • handles stuff like "transactions are reference counted by outputs" etc.
  • providing tower::Service interfaces for all of the above to support backpressure.

Exported types

  • Request, an enum representing all possible requests in the internal protocol;
    • blocks can be accessed via their chain height or hash
    • confirmed transactions can be accessed via their block, or directly via their hash
  • Response, an enum representing all possible responses in the internal protocol;
  • init() -> impl Service, the main entry-point.

The init entrypoint returns a Service that can be used to send requests for the chain state.

All state management (adding blocks, getting blocks by index or hash) is completely encapsulated.


Internal Dependencies

  • ??? depends on how it's implemented internally

Responsible for

  • the minimal Bitcoin script implementation required for Zcash
  • script parsing
  • context-free script validation


This can wrap an existing script implementation at the beginning.

If this existed in a "good" way, we could use it to implement tooling for Zcash script inspection, debugging, etc.


  • How does this interact with NU4 script changes?

Exported types

  • [...]


Internal Dependencies

  • zebra-chain for data structures and parsing.
  • zebra-state to read and update the state database.
  • zebra-script for script parsing and validation.

Responsible for

  • consensus-specific parameters (network magics, genesis block, pow parameters, etc) that determine the network consensus
  • consensus logic to decide which block is the current block
  • block and transaction verification
    • context-free validation, e.g., signature, proof verification, etc.
    • context-dependent validation, e.g., determining whether a transaction is accepted in a particular chain state context.
    • verifying mempool (unconfirmed) transactions
  • block checkpoints
    • mandatory checkpoints (genesis block, canopy activation)
    • optional regular checkpoints (every Nth block)
  • modifying the chain state
    • adding new blocks to ZebraState, including chain reorganisation
    • adding new transactions to ZebraMempoolState
  • storing the transaction mempool state
    • mempool transactions can be accessed via their hash
  • providing tower::Service interfaces for all of the above to support backpressure and batch validation.

Exported types

  • block::init() -> impl Service, the main entry-point for block verification.
  • ZebraMempoolState
    • all state management (adding transactions, getting transactions by hash) is completely encapsulated.
  • mempool::init() -> impl Service, the main entry-point for mempool transaction verification.

The init entrypoints return Services that can be used to verify blocks or transactions, and add them to the relevant state.


Internal Dependencies

  • zebra-chain for data structure definitions
  • zebra-network possibly? for definitions of network messages?

Responsible for

  • rpc interface

Exported types

  • [...]


Internal Dependencies

  • zebra-chain for structure definitions
  • zebra-state for transaction queries and client/wallet state storage
  • zebra-script possibly? for constructing transactions

Responsible for

  • implementation of some event a user might trigger
  • would be used to implement a full wallet
  • create transactions, monitors shielded wallet state, etc.


Communication between the client code and the rest of the node should be done by a tower service interface. Since the Service trait can abstract from a function call to RPC, this means that it will be possible for us to isolate all client code to a subprocess.

Exported types

  • [...]


Abscissa-based application which loads configs, all application components, and connects them to each other.

Responsible for

  • actually running the server
  • connecting functionality in dependencies

Internal Dependencies

  • zebra-chain
  • zebra-network
  • zebra-state
  • zebra-consensus
  • zebra-client
  • zebra-rpc

Unassigned functionality

Responsibility for this functionality needs to be assigned to one of the modules above (subject to discussion):

  • [ ... add to this list ... ]

Zebra RFCs

We are experimenting with using a process similar to the Rust RFC process to document design decisions for Zebra.


The Bitcoin network protocol used by Zcash allows nodes to download blocks from other peers. This RFC describes how we find and download this data asynchronously.


To sync the chain, we need to find out which blocks to download and then download them. Downloaded blocks can then be fed into the verification system and (assuming they verify correctly) into the state system. In zcashd, blocks are processed one at a time. In Zebra, however, we want to be able to pipeline block download and verification operations, using futures to explicitly specify logical dependencies between sub-tasks, which we execute concurrently and potentially out-of-order on a threadpool. This means that the procedure we use to determine which blocks to download must look somewhat different than zcashd.

Block fetching in Bitcoin

Zcash inherits its network protocol from Bitcoin. Bitcoin block fetching works roughly as follows. A node can request block information from peers using either a getblocks or getheaders message. Both of these messages contain a block locator object consisting of a sequence of block hashes. The block hashes are ordered from highest to lowest, and represent checkpoints along the path from the node's current tip back to genesis. The remote peer computes the intersection between its chain and the node's chain by scanning through the block locator for the first hash in its chain. Then, it sends (up to) 500 subsequent block hashes in an inv message (in the case of getblocks) or (up to) 2000 block headers in a headers message (in the case of getheaders). Note: zcashd reduces the getheaders count to 160, because Zcash headers are much larger than Bitcoin headers, as noted below.

The headers message sent after getheaders contains the actual block headers, while the inv message sent after getblocks contains only hashes, which have to be fetched with a getdata message. In Bitcoin, the block headers are small relative to the size of the full block, but this is not always the case for Zcash, where the block headers are much larger due to the use of Equihash and many blocks have only a few transactions. Also, getblocks allows parallelizing block downloads, while getheaders doesn't. For these reasons and because we know we need full blocks anyways, we should probably use getblocks.

The getblocks Bitcoin message corresponds to our zebra_network::Request::FindBlocksByHash, and the getdata message is generated by zebra_network::Request::Blocks.

Pipelining block verification

As mentioned above, our goal is to be able to pipeline block download and verification. This means that the process for block lookup should ideally attempt to fetch and begin verification of future blocks without blocking on complete verification of all earlier blocks. To do this, we split the chain state into the verified block chain (held by the state component) and the prospective block chain (held only by the syncer), and use the following algorithm to pursue prospective chain tips.


  1. Query the current state to construct the sequence of hashes
[tip, tip-1, tip-2, ..., tip-9, tip-20, tip-40, tip-80, tip-160 ]

The precise construction is unimportant, but this should have a Bitcoin-style dense-first, then-sparse hash structure.

The initial state should contain the genesis block for the relevant network. So the sequence of hashes will only contain the genesis block

[genesis ]

The network will respond with a list of hashes, starting at the child of the genesis block.

  1. Make a FindBlocksByHash request to the network F times, where F is a fanout parameter, to get resp1, ..., respF.

  2. For each response, starting from the beginning of the list, prune any block hashes already included in the state, stopping at the first unknown hash to get resp1', ..., respF'. (These lists may be empty).

  3. Combine the last elements of each list into a set; this is the set of prospective tips.

  4. Combine all elements of each list into a set, and queue download and verification of those blocks.

  5. If there are any prospective tips, call ExtendTips, which returns a new set of prospective tips. Continue calling ExtendTips with this new set, until there are no more prospective tips.

  6. Restart after some delay, say 15 seconds.


  1. Remove all prospective tips from the set of prospective tips, then iterate through them. For each removed tip:

  2. Create a FindBlocksByHash request consisting of just the prospective tip. Send this request to the network F times.

  3. For each response, check whether the first hash in the response is a genesis block (for either the main or test network). If so, discard the response. It indicates that the remote peer does not have any blocks following the prospective tip. (Or that the remote peer is on the wrong network.)

  4. Combine the last elements of the remaining responses into a set, and add this set to the set of prospective tips.

  5. Combine all elements of the remaining responses into a set, and queue download and verification of those blocks.

DoS resistance

Because this strategy aggressively downloads any available blocks, it could be vulnerable to a DoS attack, where a malicious peer feeds us bogus chain tips, causing us to waste network and CPU on blocks that will never be valid. However, because we separate block finding from block downloading, and because of the design of our network stack, this attack is probably not feasible. The primary reason is that zebra_network randomly loadbalances outbound requests over all available peers.

Consider a malicious peer who responds to block discovery with a bogus list of hashes. We will eagerly attempt to download all of those bogus blocks, but our requests to do so will be randomly load-balanced to other peers, who are unlikely to know about the bogus blocks. When we try to extend a bogus tip, the extension request will also be randomly load-balanced, so it will likely be routed to a peer that doesn't know about it and can't extend it. And because we perform multiple block discovery queries, which will also be randomly load balanced, we're unlikely to get stuck on a false chain tip.


When starting from a verified chain tip, the choice of block locator can find forks at least up to the reorg limit (99 blocks). When extending a prospective tip, forks are ignored, but this is fine, since unless we are prefetching the longest chain, we won't be able to keep extending the tip prospectively.

Retries and Fanout

We should consider the fanout parameter F and the retry policy for the different requests. I'm not sure whether we need to retry requests to discover new block hashes, since the fanout may already provide redundancy. For the block requests themselves, we should have a retry policy with a limited number of attempts, enough to insulate against network failures but not so many that we would retry a bogus block indefinitely. Maybe fanout 4 and 3 retries?

Parallel Verification


Zebra verifies blocks in several stages, most of which can be executed in parallel.

We use several different design patterns to enable this parallelism:

  • We download blocks and start verifying them in parallel,
  • We batch signature and proof verification using verification services, and
  • We defer data dependencies until just before the block is committed to the state (see the detailed design RFCs).


Zcash (and Bitcoin) are designed to verify each block in sequence, starting from the genesis block. But during the initial sync, and when restarting with an older state, this process can be quite slow.

By deferring data dependencies, we can partially verify multiple blocks in parallel.

By parallelising block and transaction verification, we can use multithreading and batch verification for signatures, proofs, scripts, and hashes.



  • chain fork: Zcash is implemented using a tree of blocks. Each block has a single previous block, and zero to many next blocks. A chain fork consists of a tip and all its previous blocks, back to the genesis block.
  • genesis: The root of the tree of blocks is called the genesis block. It has no previous block.
  • tip: A block which has no next block is called a tip. Each chain fork can be identified using its tip.


  • consensus rule: A protocol rule which all nodes must apply consistently, so they can converge on the same chain fork.
  • context-free: Consensus rules which do not have a data dependency on previous blocks.
  • data dependency: Information contained in the previous block and its chain fork, which is required to verify the current block.
  • state: The set of verified blocks. The state might also cache some dependent data, so that we can efficiently verify subsequent blocks.

Verification Stages:

  • structural verification: Parsing raw bytes into the data structures defined by the protocol.
  • semantic verification: Verifying the consensus rules on the data structures defined by the protocol.
  • contextual verification: Verifying the current block, once its data dependencies have been satisfied by a verified previous block. This verification might also use the cached state corresponding to the previous block.

Guide-level explanation

In Zebra, we want to verify blocks in parallel. Some fields can be verified straight away, because they don't depend on the output of previous blocks. But other fields have data dependencies, which means that we need previous blocks before we can fully validate them.

If we delay checking some of these data dependencies, then we can do more of the verification in parallel.

Example: BlockHeight

Here's how Zebra can verify the different Block Height consensus rules in parallel:

Structural Verification:

  1. Parse the Block into a BlockHeader and a list of transactions.

Semantic Verification: No Data Dependencies:

  1. Check that the first input of the first transaction in the block is a coinbase input with a valid block height in its data field.

Semantic Verification: Deferring a Data Dependency:

  1. Verify other consensus rules that depend on Block Height, assuming that the Block Height is correct. For example, many consensus rules depend on the current Network Upgrade, which is determined by the Block Height. We verify these consensus rules, assuming the Block Height and Network Upgrade are correct.

Contextual Verification:

  1. Submit the block to the state for contextual verification. When it is ready to be committed (it may arrive before the previous block), check all deferred constraints, including the constraint that the block height of this block is one more than the block height of its parent block. If all constraints are satisfied, commit the block to the state. Otherwise, reject the block as invalid.

Zebra Design

Design Patterns

When designing changes to Zebra verification, use these design patterns:

  • perform context-free verification as soon as possible, (that is, verification which has no data dependencies on previous blocks),
  • defer data dependencies as long as possible, then
  • check the data dependencies.

Minimise Deferred Data

Keep the data dependencies and checks as simple as possible.

For example, Zebra could defer checking both the Block Height and Network Upgrade.

But since the Network Upgrade depends on the Block Height, we only need to defer the Block Height check. Then we can use all the fields that depend on the Block Height, as if it is correct. If the final Block Height check fails, we will reject the entire block, including all the verification we performed using the assumed Network Upgrade.

Implementation Strategy

When implementing these designs, perform as much verification as possible, await any dependencies, then perform the necessary checks.

Reference-level explanation

Verification Stages

In Zebra, verification occurs in the following stages:

  • Structural Verification: Raw block data is parsed into a block header and transactions. Invalid data is not representable in these structures: deserialization (parsing) can fail, but serialization always succeeds.
  • Semantic Verification: Parsed block fields are verified, based on their data dependencies:
    • Context-free fields have no data dependencies, so they can be verified as needed.
    • Fields with simple data dependencies defer that dependency as long as possible, so they can perform more verification in parallel. Then they await the required data, which is typically the previous block. (And potentially older blocks in its chain fork.)
    • Fields with complex data dependencies require their own parallel verification designs. These designs are out of scope for this RFC.
  • Contextual Verification: After a block is verified, it is added to the state. The details of state updates, and their interaction with semantic verification, are out of scope for this RFC.

This RFC focuses on Semantic Verification, and the design patterns that enable blocks to be verified in parallel.

Verification Interfaces

Verification is implemented by the following traits and services:

  • Structural Verification:
    • zebra_chain::ZcashDeserialize: A trait for parsing consensus-critical data structures from a byte buffer.
  • Semantic Verification:
    • ChainVerifier: Provides a verifier service that accepts a Block request, performs verification on the block, and responds with a block::Hash on success.
    • Internally, the ChainVerifier selects between a CheckpointVerifier for blocks that are within the checkpoint range, and a BlockVerifier for recent blocks.
  • Contextual Verification:
    • zebra_state::init: Provides the state update service, which accepts requests to add blocks to the state.

Checkpoint Verification

The CheckpointVerifier performs rapid verification of blocks, based on a set of hard-coded checkpoints. Each checkpoint hash can be used to verify all the

previous blocks, back to the genesis block. So Zebra can skip almost all verification for blocks in the checkpoint range.

The CheckpointVerifier uses an internal queue to store pending blocks. Checkpoint verification is cheap, so it is implemented using non-async functions within the CheckpointVerifier service.

Here is how the CheckpointVerifier implements each verification stage:

  • Structural Verification:
    • As Above: the CheckpointVerifier accepts parsed Block structs.
  • Semantic Verification:
    • check_height: makes sure the block height is within the unverified checkpoint range, and adds the block to its internal queue.
    • target_checkpoint_height: Checks for a continuous range of blocks from the previous checkpoint to a subsequent checkpoint. If the chain is incomplete, returns a future, and waits for more blocks. If the chain is complete, assumes that the previous_block_hash fields of these blocks form an unbroken chain from checkpoint to checkpoint, and starts processing the checkpoint range. (This constraint is an implicit part of the CheckpointVerifier design.)
    • process_checkpoint_range: makes sure that the blocks in the checkpoint range have an unbroken chain of previous block hashes.
  • Contextual Verification:
    • As Above: the CheckpointVerifier returns success to the ChainVerifier, which sends verified Blocks to the state service.

Block Verification

The BlockVerifier performs detailed verification of recent blocks, in parallel.

Here is how the BlockVerifier implements each verification stage:

  • Structural Verification:
    • As Above: the BlockVerifier accepts parsed Block structs.
  • Semantic Verification:
    • As Above: verifies each field in the block. Defers any data dependencies as long as possible, awaits those data dependencies, then performs data dependent checks.
    • Note: Since futures are executed concurrently, we can use the same function to:
      • perform context-free verification,
      • perform verification with deferred data dependencies,
      • await data dependencies, and
      • check data dependencies. To maximise concurrency, we should write verification functions in this specific order, so the awaits are as late as possible.
  • Contextual Verification:
    • As Above: the BlockVerifier returns success to the ChainVerifier, which sends verified Blocks to the state service.

Zcash Protocol Design

When designing a change to the Zcash protocol, minimise the data dependencies between blocks.

Try to create designs that:

  • Eliminate data dependencies,
  • Make the changes depend on a version field in the block header or transaction,
  • Make the changes depend on the current Network Upgrade, or
  • Make the changes depend on a field in the current block, with an additional consensus rule to check that field against previous blocks.

When making decisions about these design tradeoffs, consider:

  • how the data dependency could be deferred, and
  • the CPU cost of the verification - if it is trivial, then it does not matter if the verification is parallelised.


This design is a bit complicated, but we think it's necessary to achieve our goals.

Rationale and alternatives

  • What makes this design a good design?
    • It enables a significant amount of parallelism
    • It is simpler than some other alternatives
    • It uses existing Rust language facilities, mainly Futures and await/async
  • Is this design a good basis for later designs or implementations?
    • We have built a UTXO design on this design
    • We believe we can build "recent blocks" and "chain summary" designs on this design
    • Each specific detailed design will need to consider how the relevant data dependencies are persisted
  • What other designs have been considered and what is the rationale for not choosing them?
    • Serial verification
      • Effectively single-threaded
    • Awaiting data dependencies as soon as they are needed
      • Less parallelism
    • Providing direct access to the state
      • Might cause data races, might be prevented by Rust's ownership rules
      • Higher risk of bugs
  • What is the impact of not doing this?
    • Verification is slow, we can't batch or parallelise some parts of the verification

Prior art

TODO: expand this section

  • zcashd
    • serial block verification
    • Zebra implements the same consensus rules, but a different design
  • tower

Unresolved questions

  • Is this design good enough to use as a framework for future RFCs?
  • Does this design require any changes to the current implementation?
    • Implement block height consensus rule (check previous block hash and height)
    • Check that the BlockVerifier performs checks in the following order:
      • verification, deferring dependencies as needed,
      • await dependencies,
      • check deferred data dependencies

Out of Scope:

  • What is the most efficient design for parallel verification?

    • (Optimisations are out of scope.)
  • How is each specific field verified?

  • How do we verify fields with complex data dependencies?

  • How does verification change with different network upgrades?

  • How do multiple chains work, in detail?

  • How do state updates work, in detail?

  • Moving the verifiers into the state service

Future possibilities

  • Separate RFCs for other data dependencies
    • Recent blocks
    • Overall chain summaries (for example, total work)
    • Reorganisation limit: multiple chains to single chain transition
  • Optimisations for parallel verification


The Bitcoin network protocol used by Zcash allows nodes to advertise data (inventory items) for download by other peers. This RFC describes how we track and use this information.


In order to participate in the network, we need to be able to fetch new data that our peers notify us about. Because our network stack abstracts away individual peer connections, and load-balances over available peers, we need a way to direct requests for new inventory only to peers that advertised to us that they have it.


  • Inventory item: either a block or transaction.
  • Inventory hash: the hash of an inventory item, represented by the InventoryHash type.
  • Inventory advertisement: a notification from another peer that they have some inventory item.
  • Inventory request: a request to another peer for an inventory item.

Guide-level explanation

The Bitcoin network protocol used by Zcash provides a mechanism for nodes to gossip blockchain data to each other. This mechanism is used to distribute (mined) blocks and (unmined) transactions through the network. Nodes can advertise data available in their inventory by sending an inv message containing the hashes and types of those data items. After receiving an inv message advertising data, a node can determine whether to download it.

This poses a challenge for our network stack, which goes to some effort to abstract away details of individual peers and encapsulate all peer connections behind a single request/response interface representing "the network". Currently, the peer set tracks readiness of all live peers, reports readiness if at least one peer is ready, and routes requests across ready peers randomly using the "power of two choices" algorithm.

However, while this works well for data that is already distributed across the network (e.g., existing blocks) it will not work well for fetching data during distribution across the network. If a peer informs us of some new data, and we attempt to download it from a random, unrelated peer, we will likely fail. Instead, we track recent inventory advertisements, and make a best-effort attempt to route requests to peers who advertised that inventory.

Reference-level explanation

The inventory tracking system has several components:

  1. A registration hook that monitors incoming messages for inventory advertisements;
  2. An inventory registry that tracks inventory presence by peer;
  3. Routing logic that uses the inventory registry to appropriately route requests.

The first two components have fairly straightforward design decisions, but the third has considerably less obvious choices and tradeoffs.

Inventory Monitoring

Zebra uses Tokio's codec mechanism to transform a byte-oriented I/O interface into a Stream and Sink for incoming and outgoing messages. These are passed to the peer connection state machine, which is written generically over any Stream and Sink. This construction makes it easy to "tap" the sequence of incoming messages using .then and .with stream and sink combinators.

We already do this to record Prometheus metrics on message rates as well as to report message timestamps used for liveness checks and last-seen address book metadata. The message timestamp mechanism is a good example to copy. The handshake logic instruments the incoming message stream with a closure that captures a sender handle for a mpsc channel with a large buffer (currently 100 timestamp entries). The receiver handle is owned by a separate task that shares an Arc<Mutex<AddressBook>> with other parts of the application. This task waits for new timestamp entries, acquires a lock on the address book, and updates the address book. This ensures that timestamp updates are queued asynchronously, without lock contention.

Unlike the address book, we don't need to share the inventory data with other parts of the application, so it can be owned exclusively by the peer set. This means that no lock is necessary, and the peer set can process advertisements in its poll_ready implementation. This method may be called infrequently, which could cause the channel to fill. However, because inventory advertisements are time-limited, in the sense that they're only useful before some item is fully distributed across the network, it's safe to handle excess entries by dropping them. This behavior is provided by a broadcast/mpmc channel, which can be used in place of an mpsc channel.

An inventory advertisement is an (InventoryHash, SocketAddr) pair. The stream hook should check whether an incoming message is an inv message with only a small number (e.g., 1) inventory entries. If so, it should extract the hash for each item and send it through the channel. Otherwise, it should ignore the message contents. Why? Because inv messages are also sent in response to queries, such as when we request subsequent block hashes, and in that case we want to assume that the inventory is generally available rather than restricting downloads to a single peer. However, items are usually gossiped individually (or potentially in small chunks; zcashd has an internal inv buffer subject to race conditions), so choosing a small bound such as 1 is likely to work as a heuristic for when we should assume that advertised inventory is not yet generally available.

Inventory Registry

The peer set's poll_ready implementation should extract all available (InventoryHash, SocketAddr) pairs from the channel, and log a warning event if the receiver is lagging. The channel should be configured with a generous buffer size (such as 100) so that this is unlikely to happen in normal circumstances. These pairs should be fed into an InventoryRegistry structure along these lines:

fn main() {
struct InventoryRegistry{
    current: HashMap<InventoryHash, HashSet<SocketAddr>>,
    prev: HashMap<InventoryHash, HashSet<SocketAddr>>,

impl InventoryRegistry {
    pub fn register(&mut self, item: InventoryHash, addr: SocketAddr) {

    pub fn rotate(&mut self) {
        self.prev = std::mem::take(self.current)

    pub fn peers(&self, item: InventoryHash) -> impl Iterator<Item=&SocketAddr> {

This API allows pruning the inventory registry using rotate, which implements generational pruning of registry entries. The peer set should maintain a tokio::time::Interval with some interval parameter, and check in poll_ready whether the interval stream has any items, calling rotate for each one:

fn main() {
while let Poll::Ready(Some(_)) = timer.poll_next(cx) {

By rotating for each available item in the interval stream, rather than just once, we ensure that if the peer set's poll_ready is not called for a long time, rotate will be called enough times to correctly flush old entries.

Inventory advertisements live in the registry for twice the length of the timer, so it should be chosen to be half of the desired lifetime for inventory advertisements. Setting the timer to 75 seconds, the block interval, seems like a reasonable choice.

Routing Logic

At this point, the peer set has information on recent inventory advertisements. However, the Service trait only allows poll_ready to report readiness based on the service's data and the type of the request, not the content of the request. This means that we must report readiness without knowing whether the request should be routed to a specific peer, and we must handle the case where call gets a request for an item only available at an unready peer.

This RFC suggests the following routing logic. First, check whether the request fetches data by hash. If so, and peers() returns Some(ref addrs), iterate over addrs and route the request to the first ready peer if there is one. In all other cases, fall back to p2c routing. Alternatives are suggested and discussed below.

Rationale and alternatives

The rationale is described above. The alternative choices are primarily around the routing logic.

Because the Service trait does not allow applying backpressure based on the content of a request, only based on the service's internal data (via the &mut self parameter of Service::poll_ready) and on the type of the request (which determines which impl Service is used). This means that it is impossible for us to apply backpressure until a service that can process a specific inventory request is ready, because until we get the request, we can't determine which peers might be required to process it.

We could attempt to ensure that the peer set would be ready to process a specific inventory request would be to pre-emptively "reserve" a peer as soon as it advertises an inventory item. But this doesn't actually work to ensure readiness, because a peer could advertise two inventory items, and only be able to service one request at a time. It also potentially locks the peer set, since if there are only a few peers and they all advertise inventory, the service can't process any other requests. So this approach does not work.

Another alternative would be to do some kind of buffering of inventory requests that cannot immediately be processed by a peer that advertised that inventory. There are two basic sub-approaches here.

In the first case, we could maintain an unbounded queue of yet-to-be processed inventory requests in the peer set, and every time poll_ready is called, we check whether a service that could serve those inventory requests became ready, and start processing the request if we can. This would provide the lowest latency, because we can dispatch the request to the first available peer. For instance, if peer A advertises inventory I, the peer set gets an inventory request for I, peer A is busy so the request is queued, and peer B advertises inventory I, we could dispatch the queued request to B rather than waiting for A.

However, it's not clear exactly how we'd implement this, because this mechanism is driven by calls to poll_ready, and those might not happen. So we'd need some separate task that would drive processing the buffered task to completion, but this may not be able to do so by poll_ready, since that method requires owning the service, and the peer set will be owned by a Buffer worker.

In the second case, we could select an unready peer that advertised the requested inventory, clone it, and move the cloned peer into a task that would wait for that peer to become ready and then make the request. This is conceptually much cleaner than the above mechanism, but it has the downside that we don't dispatch the request to the first ready peer. In the example above, if we cloned peer A and dispatched the request to it, we'd have to wait for A to become ready, even if the second peer B advertised the same inventory just after we dispatched the request to A. However, this is not presently possible anyways, because the peer::Clients that handle requests are not clonable. They could be made clonable (they send messages to the connection state machine over a mpsc channel), but we cannot make this change without altering our liveness mechanism, which uses bounds on the time-since-last-message to determine whether a peer connection is live and to prevent immediate reconnections to recently disconnected peers.

A final alternative would be to fail inventory requests that we cannot route to a peer which advertised that inventory. This moves the failure forward in time, but preemptively fails some cases where the request might succeed -- for instance, if the peer has inventory but just didn't tell us, or received the inventory between when we dispatch the request and when it receives our message. It seems preferable to try and fail than to not try at all.

In practice, we're likely to care about the gossip protocol and inventory fetching once we've already synced close to the chain tip. In this setting, we're likely to already have peer connections, and we're unlikely to be saturating our peer set with requests (as we do during initial block sync). This suggests that the common case is one where we have many idle peers, and that therefore we are unlikely to have dispatched any recent requests to the peer that advertised inventory. So our common case should be one where all of this analysis is irrelevant.


This RFC describes an architecture for asynchronous script verification and its interaction with the state layer. This architecture imposes constraints on the ordering of operations in the state layer.


As in the rest of Zebra, we want to express our work as a collection of work-items with explicit dependencies, then execute these items concurrently and in parallel on a thread pool.


  • UTXO: unspent transaction output. Transaction outputs are modeled in zebra-chain by the transparent::Output structure.
  • Transaction input: an output of a previous transaction consumed by a later transaction (the one it is an input to). Modeled in zebra-chain by the transparent::Input structure.
  • lock script: the script that defines the conditions under which some UTXO can be spent. Stored in the transparent::Output::lock_script field.
  • unlock script: a script satisfying the conditions of the lock script, allowing a UTXO to be spent. Stored in the transparent::Input::PrevOut::lock_script field.

Guide-level explanation

Zcash's transparent address system is inherited from Bitcoin. Transactions spend unspent transaction outputs (UTXOs) from previous transactions. These UTXOs are encumbered by locking scripts that define the conditions under which they can be spent, e.g., requiring a signature from a certain key. Transactions wishing to spend UTXOs supply an unlocking script that should satisfy the conditions of the locking script for each input they wish to spend.

This means that script verification requires access to data about previous UTXOs, in order to determine the conditions under which those UTXOs can be spent. In Zebra, we aim to run operations asychronously and out-of-order to the greatest extent possible. For instance, we may begin verification of a block before all of its ancestors have been verified or even downloaded. So we need to design a mechanism that allows script verification to declare its data dependencies and execute as soon as all required data is available.

It's not necessary for this mechanism to ensure that the transaction outputs remain unspent, only to give enough information to perform script verification. Checking that all transaction inputs are actually unspent is done later, at the point that its containing block is committed to the chain.

At a high level, this adds a new request/response pair to the state service:

  • Request::AwaitUtxo(OutPoint) requests a transparent::Output specified by OutPoint from the state layer;
  • Response::Utxo(transparent::Output) supplies requested the transparent::Output.

Note that this request is named differently from the other requests, AwaitUtxo rather than GetUtxo or similar. This is because the request has rather different behavior: the request does not complete until the state service learns about a UTXO matching the request, which could be never. For instance, if the transaction output was already spent, the service is not required to return a response. The caller is responsible for using a timeout layer or some other mechanism.

This allows a script verifier to asynchronously obtain information about previous transaction outputs and start verifying scripts as soon as the data is available. For instance, if we begin parallel download and verification of 500 blocks, we should be able to begin script verification of all scripts referencing outputs from existing blocks in parallel, and begin verification of scripts referencing outputs from new blocks as soon as they are committed to the chain.

Because spending outputs from older blocks is more common than spending outputs from recent blocks, this should allow a significant amount of parallelism.

Reference-level explanation

We add a Request::AwaitUtxo(OutPoint) and Response::Utxo(transparent::Output) to the state protocol. As described above, the request name is intended to indicate the request's behavior: the request does not resolve until the state layer learns of a UTXO described by the request.

To verify scripts, a script verifier requests the relevant UTXOs from the state service and waits for all of them to resolve, or fails verification with a timeout error. Currently, we outsource script verification to zcash_consensus, which does FFI into the same C++ code as zcashd uses. We need to ensure this code is thread-safe.

Implementing the state request correctly requires considering two sets of behaviors:

  1. behaviors related to the state's external API (a Buffered tower::Service);
  2. behaviors related to the state's internal implementation (using rocksdb).

Making this distinction helps us to ensure we don't accidentally leak "internal" behaviors into "external" behaviors, which would violate encapsulation and make it more difficult to replace rocksdb.

In the first category, our state is presented to the rest of the application as a Buffered tower::Service. The Buffer wrapper allows shared access to a service using an actor model, moving the service to be shared into a worker task and passing messages to it over an multi-producer single-consumer (mpsc) channel. The worker task receives messages and makes Service::calls. The Service::call method returns a Future, and the service is allowed to decide how much work it wants to do synchronously (in call) and how much work it wants to do asynchronously (in the Future it returns).

This means that our external API ensures that the state service sees a linearized sequence of state requests, although the exact ordering is unpredictable when there are multiple senders making requests.

Because the state service has exclusive access to the rocksdb database, and the state service sees a linearized sequence of state requests, we have an easy way to opt in to asynchronous database access. We can perform rocksdb operations synchronously in the Service::call, waiting for them to complete, and be sure that all future requests will see the resulting rocksdb state. Or, we can perform rocksdb operations asynchronously in the future returned by Service::call.

If we perform all writes synchronously and allow reads to be either synchronous or asynchronous, we ensure that writes cannot race each other. Asynchronous reads are guaranteed to read at least the state present at the time the request was processed, or a later state.

Now, returning to the UTXO lookup problem, we can map out the possible states with this restriction in mind. This description assumes that UTXO storage is split into disjoint sets, one in-memory (e.g., blocks after the reorg limit) and the other in rocksdb (e.g., blocks after the reorg limit). The details of this storage are not important for this design, only that the two sets are disjoint.

When the state service processes a Request::AwaitUtxo(OutPoint) referencing some UTXO u, there are three disjoint possibilities:

  1. u is already contained in an in-memory block storage;
  2. u is already contained in the rocksdb UTXO set;
  3. u is not yet known to the state service.

In case 3, we need to queue u and scan all future blocks to see whether they contain u. However, if we have a mechanism to queue u, we can perform check 2 asynchronously, because restricting to synchronous writes means that any async read will return the current or later state. If u was in the rocksdb UTXO set when the request was processed, the only way that an async read would not return u is if the UTXO were spent, in which case the service is not required to return a response.

This behavior can be encapsulated into a PendingUtxos structure described below.

fn main() {
// sketch
#[derive(Default, Debug)]
struct PendingUtxos(HashMap<OutPoint, oneshot::Sender<transparent::Output>>);

impl PendingUtxos {
    // adds the outpoint and returns (wrapped) rx end of oneshot
    // return can be converted to `Service::Future`
    pub fn queue(&mut self, outpoint: OutPoint) -> impl Future<Output=Result<Response, ...>>;

    // if outpoint is a hashmap key, remove the entry and send output on the channel
    pub fn respond(&mut self, outpoint: OutPoint, output: transparent::Output);

    // scans the hashmap and removes any entries with closed senders
    pub fn prune(&mut self);

The state service should maintain an Arc<Mutex<PendingUtxos>>, used as follows:

  1. In Service::call(Request::AwaitUtxo(u)), the service should:
  • call PendingUtxos::queue(u) to get a future f to return to the caller; spawn a task that does a rocksdb lookup for u, calling PendingUtxos::respond(u, output) if present;
  • check the in-memory storage for u, calling PendingUtxos::respond(u, output) if present;
  • return f to the caller (it may already be ready). The common case is that u references an old UTXO, so spawning the lookup task first means that we don't wait to check in-memory storage for u before starting the rocksdb lookup.
  1. In Service::call(Request::CommitBlock(block, ..)), the service should:
  • call PendingUtxos::check_block(block.as_ref());
  • do any other transactional checks before committing a block as normal. Because the AwaitUtxo request is informational, there's no need to do the transactional checks before matching against pending UTXO requests, and doing so upfront potentially notifies other tasks earlier.
  1. In Service::poll_ready(), the service should call PendingUtxos::prune() at least some of the time. This is required because when a consumer uses a timeout layer, the cancelled requests should be flushed from the queue to avoid a resource leak. However, doing this on every call will result in us spending a bunch of time iterating over the hashmap.


One drawback of this design is that we may have to wait on a lock. However, the critical section basically amounts to a hash lookup and a channel send, so I don't think that we're likely to run into problems with long contended periods, and it's unlikely that we would get a deadlock.

Rationale and alternatives

High-level design rationale is inline with the design sketch. One low-level option would be to avoid encapsulating behavior in the PendingUtxos and just have an Arc<Hashmap<..>>, so that the lock only protects the hashmap lookup and not sending through the channel. But I think the current design is cleaner and the cost is probably not too large.

Unresolved questions

  • We need to pick a timeout for UTXO lookup. This should be long enough to account for the fact that we may start verifying blocks before all of their ancestors are downloaded.

State Updates

  • Feature Name: state_updates
  • Start Date: 2020-08-14
  • Design PR: https://github.com/ZcashFoundation/zebra/pull/902
  • Zebra Issue: https://github.com/ZcashFoundation/zebra/issues/1049


Zebra manages chain state in the zebra-state crate, which allows state queries via asynchronous RPC (in the form of a Tower service). The state system is responsible for contextual verification in the sense of RFC2, checking that new blocks are consistent with the existing chain state before committing them. This RFC describes how the state is represented internally, and how state updates are performed.


We need to be able to access and modify the chain state, and we want to have a description of how this happens and what guarantees are provided by the state service.


  • state data: Any data the state service uses to represent chain state.

  • structural/semantic/contextual verification: as defined in RFC2.

  • block chain: A sequence of valid blocks linked by inclusion of the previous block hash in the subsequent block. Chains are rooted at the genesis block and extend to a tip.

  • chain state: The state of the ledger after application of a particular sequence of blocks (state transitions).

  • block work: The approximate amount of work required for a miner to generate a block hash that passes the difficulty filter. The number of block header attempts and the mining time are proportional to the work value. Numerically higher work values represent longer processing times.

  • cumulative work: The sum of the block work of all blocks in a chain, from genesis to the chain tip.

  • best chain: The chain with the greatest cumulative work. This chain represents the consensus state of the Zcash network and transactions.

  • side chain: A chain which is not contained in the best chain. Side chains are pruned at the reorg limit, when they are no longer connected to the finalized state.

  • chain reorganization: Occurs when a new best chain is found and the previous best chain becomes a side chain.

  • reorg limit: The longest reorganization accepted by zcashd, 100 blocks.

  • orphaned block: A block which is no longer included in the best chain.

  • non-finalized state: State data corresponding to blocks above the reorg limit. This data can change in the event of a chain reorg.

  • finalized state: State data corresponding to blocks below the reorg limit. This data cannot change in the event of a chain reorg.

  • non-finalized tips: The highest blocks in each non-finalized chain. These tips might be at different heights.

  • finalized tip: The highest block in the finalized state. The tip of the best chain is usually 100 blocks (the reorg limit) above the finalized tip. But it can be lower during the initial sync, and after a chain reorganization, if the new best chain is at a lower height.

  • relevant chain: The relevant chain for a block starts at the previous block, and extends back to genesis.

  • relevant tip: The tip of the relevant chain.

Guide-level explanation

The zebra-state crate provides an implementation of the chain state storage logic in a Zcash consensus node. Its main responsibility is to store chain state, validating new blocks against the existing chain state in the process, and to allow later querying of said chain state. zebra-state provides this interface via a tower::Service based on the actor model with a request/response interface for passing messages back and forth between the state service and the rest of the application.

The main entry point for the zebra-state crate is the init function. This function takes a zebra_state::Config and constructs a new state service, which it returns wrapped by a tower::Buffer. This service is then interacted with via the tower::Service trait.

fn main() {
use tower::{Service, ServiceExt};

let state = zebra_state::on_disk::init(state_config, network);
let request = zebra_state::Request::BlockLocator;
let response = state.ready_and().await?.call(request).await?;

assert!(matches!(response, zebra_state::Response::BlockLocator(_)));

Note: The tower::Service API requires that ready is always called exactly once before each call. It is up to users of the zebra state service to uphold this contract.

The tower::Buffer wrapper is Cloneable, allowing shared access to a common state service. This allows different tasks to share access to the chain state.

The set of operations supported by zebra-state are encoded in its Request enum. This enum has one variant for each supported operation.

fn main() {
pub enum Request {
    CommitBlock {
        block: Arc<Block>,
    CommitFinalizedBlock {
        block: Arc<Block>,

    // .. some variants omitted

zebra-state breaks down its requests into two categories and provides different guarantees for each category: requests that modify the state, and requests that do not. Requests that update the state are guaranteed to run sequentially and will never race against each other. Requests that read state are done asynchronously and are guaranteed to read at least the state present at the time the request was processed by the service, or a later state present at the time the request future is executed. The state service avoids race conditions between the read state and the written state by doing all contextual verification internally.

Reference-level explanation

State Components

Zcash (as implemented by zcashd) differs from Bitcoin in its treatment of transaction finality. If a new best chain is detected that does not extend the previous best chain, blocks at the end of the previous best chain become orphaned (no longer included in the best chain). Their state updates are therefore no longer included in the best chain's chain state. The process of rolling back orphaned blocks and applying new blocks is called a chain reorganization. Bitcoin allows chain reorganizations of arbitrary depth, while zcashd limits chain reorganizations to 100 blocks. (In zcashd, the new best chain must be a side-chain that forked within 100 blocks of the tip of the current best chain.)

This difference means that in Bitcoin, chain state only has probabilistic finality, while in Zcash, chain state is final once it is beyond the reorg limit. To simplify our implementation, we split the representation of the state data at the finality boundary provided by the reorg limit.

State data from blocks above the reorg limit (non-finalized state) is stored in-memory and handles multiple chains. State data from blocks below the reorg limit (finalized state) is stored persistently using rocksdb and only tracks a single chain. This allows a simplification of our state handling, because only finalized data is persistent and the logic for finalized data handles less invariants.

One downside of this design is that restarting the node loses the last 100 blocks, but node restarts are relatively infrequent and a short re-sync is cheap relative to the cost of additional implementation complexity.

Another downside of this design is that we do not achieve exactly the same behavior as zcashd in the event of a 51% attack: zcashd limits each chain reorganization to 100 blocks, but permits multiple reorgs, while Zebra limits all chain reorgs to 100 blocks. In the event of a successful 51% attack on Zcash, this could be resolved by wiping the rocksdb state and re-syncing the new chain, but in this scenario there are worse problems.

Service Interface

The state is accessed asynchronously through a Tower service interface. Determining what guarantees the state service can and should provide to the rest of the application requires considering two sets of behaviors:

  1. behaviors related to the state's external API (a Buffered tower::Service);
  2. behaviors related to the state's internal implementation (using rocksdb).

Making this distinction helps us to ensure we don't accidentally leak "internal" behaviors into "external" behaviors, which would violate encapsulation and make it more difficult to replace rocksdb.

In the first category, our state is presented to the rest of the application as a Buffered tower::Service. The Buffer wrapper allows shared access to a service using an actor model, moving the service to be shared into a worker task and passing messages to it over an multi-producer single-consumer (mpsc) channel. The worker task receives messages and makes Service::calls. The Service::call method returns a Future, and the service is allowed to decide how much work it wants to do synchronously (in call) and how much work it wants to do asynchronously (in the Future it returns).

This means that our external API ensures that the state service sees a linearized sequence of state requests, although the exact ordering is unpredictable when there are multiple senders making requests.

Because the state service has exclusive access to the rocksdb database, and the state service sees a linearized sequence of state requests, we have an easy way to opt in to asynchronous database access. We can perform rocksdb operations synchronously in the Service::call, waiting for them to complete, and be sure that all future requests will see the resulting rocksdb state. Or, we can perform rocksdb operations asynchronously in the future returned by Service::call.

If we perform all writes synchronously and allow reads to be either synchronous or asynchronous, we ensure that writes cannot race each other. Asynchronous reads are guaranteed to read at least the state present at the time the request was processed, or a later state.


  • rocksdb reads may be done synchronously (in call) or asynchronously (in the Future), depending on the context;

  • rocksdb writes must be done synchronously (in call)

In-memory data structures

At a high level, the in-memory data structures store a collection of chains, each rooted at the highest finalized block. Each chain consists of a map from heights to blocks. Chains are stored using an ordered map from cumulative work to chains, so that the map ordering is the ordering of worst to best chains.

The Chain type

The Chain type represents a chain of blocks. Each block represents an incremental state update, and the Chain type caches the cumulative state update from its root to its tip.

The Chain type is used to represent the non-finalized portion of a complete chain of blocks rooted at the genesis block. The parent block of the root of a Chain is the tip of the finalized portion of the chain. As an exception, the finalized portion of the chain is initially empty, until the genesis block has been finalized.

The Chain type supports several operations to manipulate chains, push, pop_root, and fork. push is the most fundamental operation and handles contextual validation of chains as they are extended. pop_root is provided for finalization, and is how we move blocks from the non-finalized portion of the state to the finalized portion. fork on the other hand handles creating new chains for push when new blocks arrive whose parent isn't a tip of an existing chain.

Note: The Chain type's API is only designed to handle non-finalized data. The genesis block and all pre canopy blocks are always considered to be finalized blocks and should not be handled via the Chain type through CommitBlock. They should instead be committed directly to the finalized state with CommitFinalizedBlock. This is particularly important with the genesis block since the Chain will panic if used while the finalized state is completely empty.

The Chain type is defined by the following struct and API:

fn main() {
#[derive(Debug, Default, Clone)]
struct Chain {
    blocks: BTreeMap<block::Height, Arc<Block>>,
    height_by_hash: HashMap<block::Hash, block::Height>,
    tx_by_hash: HashMap<transaction::Hash, (block::Height, usize)>,

    created_utxos: HashSet<transparent::OutPoint>,
    spent_utxos: HashSet<transparent::OutPoint>,
    sprout_anchors: HashSet<sprout::tree::Root>,
    sapling_anchors: HashSet<sapling::tree::Root>,
    sprout_nullifiers: HashSet<sprout::Nullifier>,
    sapling_nullifiers: HashSet<sapling::Nullifier>,
    partial_cumulative_work: PartialCumulativeWork,

pub fn push(&mut self, block: Arc<Block>)

Push a block into a chain as the new tip

  1. Update cumulative data members

    • Add the block's hash to height_by_hash
    • Add work to self.partial_cumulative_work
    • For each transaction in block
      • Add key: transaction.hash and value: (height, tx_index) to tx_by_hash
      • Add created utxos to self.created_utxos
      • Add spent utxos to self.spent_utxos
      • Add nullifiers to the appropriate self.<version>_nullifiers
  2. Add block to self.blocks

pub fn pop_root(&mut self) -> Arc<Block>

Remove the lowest height block of the non-finalized portion of a chain.

  1. Remove the lowest height block from self.blocks

  2. Update cumulative data members

    • Remove the block's hash from self.height_by_hash
    • Subtract work from self.partial_cumulative_work
    • For each transaction in block
      • Remove transaction.hash from tx_by_hash
      • Remove created utxos from self.created_utxos
      • Remove spent utxos from self.spent_utxos
      • Remove the nullifiers from the appropriate self.<version>_nullifiers
  3. Return the block

pub fn fork(&self, new_tip: block::Hash) -> Option<Self>

Fork a chain at the block with the given hash, if it is part of this chain.

  1. If self does not contain new_tip return None

  2. Clone self as forked

  3. While the tip of forked is not equal to new_tip

    • call forked.pop_tip() and discard the old tip
  4. Return forked

fn pop_tip(&mut self)

Remove the highest height block of the non-finalized portion of a chain.

  1. Remove the highest height block from self.blocks

  2. Update cumulative data members

    • Remove the corresponding hash from self.height_by_hash
    • Subtract work from self.partial_cumulative_work
    • for each transaction in block
      • remove transaction.hash from tx_by_hash
      • Remove created utxos from self.created_utxos
      • Remove spent utxos from self.spent_utxos
      • Remove the nullifiers from the appropriate self.<version>_nullifiers


The Chain type implements Ord for reorganizing chains. First chains are compared by their partial_cumulative_work. Ties are then broken by comparing block::Hashes of the tips of each chain. (This tie-breaker means that all Chains in the NonFinalizedState must have at least one block.)

Note: Unlike zcashd, Zebra does not use block arrival times as a tie-breaker for the best tip. Since Zebra downloads blocks in parallel, download times are not guaranteed to be unique. Using the block::Hash provides a consistent tip order. (As a side-effect, the tip order is also consistent after a node restart, and between nodes.)


The Chain type implements Default for constructing new chains whose parent block is the tip of the finalized state. This implementation should be handled by #[derive(Default)].

  1. initialise cumulative data members
    • Construct an empty self.blocks, height_by_hash, tx_by_hash, self.created_utxos, self.spent_utxos, self.<version>_anchors, self.<version>_nullifiers
    • Zero self.partial_cumulative_work

Note: The ChainState can be empty after a restart, because the non-finalized state is empty.

NonFinalizedState Type

The NonFinalizedState type represents the set of all non-finalized state. It consists of a set of non-finalized but verified chains and a set of unverified blocks which are waiting for the full context needed to verify them to become available.

NonFinalizedState is defined by the following structure and API:

fn main() {
/// The state of the chains in memory, including queued blocks.
#[derive(Debug, Default)]
pub struct NonFinalizedState {
    /// Verified, non-finalized chains.
    chain_set: BTreeSet<Chain>,
    /// Blocks awaiting their parent blocks for contextual verification.
    contextual_queue: QueuedBlocks,

pub fn finalize(&mut self) -> Arc<Block>

Finalize the lowest height block in the non-finalized portion of the best chain and updates all side chains to match.

  1. Extract the best chain from self.chain_set into best_chain

  2. Extract the rest of the chains into a side_chains temporary variable, so they can be mutated

  3. Remove the lowest height block from the best chain with let finalized_block = best_chain.pop_root();

  4. Add best_chain back to self.chain_set if best_chain is not empty

  5. For each remaining chain in side_chains

    • remove the lowest height block from chain
    • If that block is equal to finalized_block and chain is not empty add chain back to self.chain_set
    • Else, drop chain
  6. Return finalized_block

fn commit_block(&mut self, block: Arc<Block>)

Commit block to the non-finalized state.

  1. If the block is a pre-Canopy block, panic.

  2. If any chains tip hash equal block.header.previous_block_hash remove that chain from self.chain_set

  3. Else Find the first chain that contains block.parent and fork it with block.parent as the new tip

    • let fork = self.chain_set.iter().find_map(|chain| chain.fork(block.parent));
  4. Else panic, this should be unreachable because commit_block is only called when block is ready to be committed.

  5. Push block into parent_chain

  6. Insert parent_chain into self.chain_set

pub(super) fn commit_new_chain(&mut self, block: Arc<Block>)

Construct a new chain starting with block.

  1. Construct a new empty chain

  2. push block into that new chain

  3. Insert the new chain into self.chain_set

The QueuedBlocks type

The queued blocks type represents the non-finalized blocks that were commited before their parent blocks were. It is responsible for tracking which blocks are queued by their parent so they can be commited immediately after the parent is commited. It also tracks blocks by their height so they can be discarded if they ever end up below the reorg limit.

NonFinalizedState is defined by the following structure and API:

fn main() {
/// A queue of blocks, awaiting the arrival of parent blocks.
#[derive(Debug, Default)]
struct QueuedBlocks {
    /// Blocks awaiting their parent blocks for contextual verification.
    blocks: HashMap<block::Hash, QueuedBlock>,
    /// Hashes from `queued_blocks`, indexed by parent hash.
    by_parent: HashMap<block::Hash, Vec<block::Hash>>,
    /// Hashes from `queued_blocks`, indexed by block height.
    by_height: BTreeMap<block::Height, Vec<block::Hash>>,

pub fn queue(&mut self, new: QueuedBlock)

Add a block to the queue of blocks waiting for their requisite context to become available.

  1. extract the parent_hash, new_hash, and new_height from new.block

  2. Add new to self.blocks using new_hash as the key

  3. Add new_hash to the set of hashes in self.by_parent.entry(parent_hash).or_default()

  4. Add new_hash to the set of hashes in self.by_height.entry(new_height).or_default()

pub fn dequeue_children(&mut self, parent: block::Hash) -> Vec<QueuedBlock>

Dequeue the set of blocks waiting on parent.

  1. Remove the set of hashes waiting on parent from self.by_parent

  2. Remove and collect each block in that set of hashes from self.blocks as queued_children

  3. For each block in queued_children remove the associated block.hash from self.by_height

  4. Return queued_children

pub fn prune_by_height(&mut self, finalized_height: block::Height)

Prune all queued blocks whose height are less than or equal to finalized_height.

  1. Split the by_height list at the finalized height, removing all heights that are below finalized_height

  2. for each hash in the removed values of by_height

    • remove the corresponding block from self.blocks
    • remove the block's hash from the list of blocks waiting on block.header.previous_block_hash from self.by_parent


  • Chain represents the non-finalized portion of a single chain

  • NonFinalizedState represents the non-finalized portion of all chains

  • QueuedBlocks represents all unverified blocks that are waiting for context to be available.

The state service uses the following entry points:

  • commit_block when it receives new blocks.

  • finalize to prevent chains in NonFinalizedState from growing beyond the reorg limit.

  • FinalizedState.queue_and_commit_finalized_blocks on the blocks returned by finalize, to commit those finalized blocks to disk.

Committing non-finalized blocks

New non-finalized blocks are commited as follows:

pub(super) fn queue_and_commit_non_finalized_blocks(&mut self, new: Arc<Block>) -> tokio::sync::oneshot::Receiver<block::Hash>

  1. If a duplicate block hash exists in a non-finalized chain, or the finalized chain, it has already been successfully verified:

    • create a new oneshot channel
    • immediately send Err(DuplicateBlockHash) drop the sender
    • return the receiver
  2. If a duplicate block hash exists in the queue:

    • Find the QueuedBlock for that existing duplicate block
    • create a new channel for the new request
    • replace the old sender in queued_block with the new sender
    • send Err(DuplicateBlockHash) through the old sender channel
    • continue to use the new receiver
  3. Else create a QueuedBlock for block:

    • Create a tokio::sync::oneshot channel
    • Use that channel to create a QueuedBlock for block
    • Add block to self.queued_blocks
    • continue to use the new receiver
  4. If block.header.previous_block_hash is not present in the finalized or non-finalized state:

    • Return the receiver for the block's channel
  5. Else iteratively attempt to process queued blocks by their parent hash starting with block.header.previous_block_hash

  6. While there are recently commited parent hashes to process

    • Dequeue all blocks waiting on parent with let queued_children = self.queued_blocks.dequeue_children(parent);
    • for each queued block
      • Run contextual validation on block
        • contextual validation should check that the block height is equal to the previous block height plus 1. This check will reject blocks with invalid heights.
      • If the block fails contextual validation send the result to the associated channel
      • Else if the block's previous hash is the finalized tip add to the non-finalized state with self.mem.commit_new_chain(block)
      • Else add the new block to an existing non-finalized chain or new fork with self.mem.commit_block(block);
      • Send Ok(hash) over the associated channel to indicate the block was successfully commited
      • Add block.hash to the set of recently commited parent hashes to process
  7. While the length of the non-finalized portion of the best chain is greater than the reorg limit

    • Remove the lowest height block from the non-finalized state with self.mem.finalize();
    • Commit that block to the finalized state with self.disk.commit_finalized_direct(finalized);
  8. Prune orphaned blocks from self.queued_blocks with self.queued_blocks.prune_by_height(finalized_height);

  9. Return the receiver for the block's channel

rocksdb data structures

rocksdb provides a persistent, thread-safe BTreeMap<&[u8], &[u8]>. Each map is a distinct "tree". Keys are sorted using lex order on byte strings, so integer values should be stored using big-endian encoding (so that the lex order on byte strings is the numeric ordering).

We use the following rocksdb column families:

tx_by_hashtransaction::Hash(BE32(height) \|\| BE32(tx_index))

Zcash structures are encoded using ZcashSerialize/ZcashDeserialize.

Note: We do not store the cumulative work for the finalized chain, because the finalized work is equal for all non-finalized chains. So the additional non-finalized work can be used to calculate the relative chain order, and choose the best chain.

Notes on rocksdb column families

  • The hash_by_height and height_by_hash column families provide a bijection between block heights and block hashes. (Since the rocksdb state only stores finalized state, they are actually a bijection).

  • The block_by_height column family provides a bijection between block heights and block data. There is no corresponding height_by_block column family: instead, hash the block, and use height_by_hash. (Since the rocksdb state only stores finalized state, they are actually a bijection).

  • Blocks are stored by height, not by hash. This has the downside that looking up a block by hash requires an extra level of indirection. The upside is that blocks with adjacent heights are adjacent in the database, and many common access patterns, such as helping a client sync the chain or doing analysis, access blocks in (potentially sparse) height order. In addition, the fact that we commit blocks in order means we're writing only to the end of the rocksdb column family, which may help save space.

  • Transaction references are stored as a (height, index) pair referencing the height of the transaction's parent block and the transaction's index in that block. This would more traditionally be a (hash, index) pair, but because we store blocks by height, storing the height saves one level of indirection.

Committing finalized blocks

If the parent block is not committed, add the block to an internal queue for future processing. Otherwise, commit the block described below, then commit any queued children. (Although the checkpointer generates verified blocks in order when it completes a checkpoint, the blocks are committed in the response futures, so they may arrive out of order).

Committing a block to the rocksdb state should be implemented as a wrapper around a function also called by Request::CommitBlock, which should:

pub(super) fn queue_and_commit_finalized_blocks(&mut self, queued_block: QueuedBlock)

  1. Obtain the highest entry of hash_by_height as (old_height, old_tip). Check that block's parent hash is old_tip and its height is old_height+1, or panic. This check is performed as defense-in-depth to prevent database corruption, but it is the caller's responsibility (e.g. the zebra-state service's responsibility) to commit finalized blocks in order.

The genesis block does not have a parent block. For genesis blocks, check that block's parent hash is null (all zeroes) and its height is 0.

  1. Insert:

    • (hash, height) into height_by_hash;
    • (height, hash) into hash_by_height;
    • (height, block) into block_by_height.
  2. If the block is a genesis block, skip any transaction updates.

    (Due to a bug in zcashd, genesis block anchors and transactions are ignored during validation.)

  3. Update the sprout_anchors and sapling_anchors trees with the Sprout and Sapling anchors.

  4. Iterate over the enumerated transactions in the block. For each transaction:

    1. Insert (transaction_hash, BE32(block_height) || BE32(tx_index)) to tx_by_hash;

    2. For each TransparentInput::PrevOut { outpoint, .. } in the transaction's inputs(), remove outpoint from utxo_by_output.

    3. For each output in the transaction's outputs(), construct the outpoint that identifies it, and insert (outpoint, output) into utxo_by_output.

    4. For each JoinSplit description in the transaction, insert (nullifiers[0],()) and (nullifiers[1],()) into sprout_nullifiers.

    5. For each Spend description in the transaction, insert (nullifier,()) into sapling_nullifiers.

Note: The Sprout and Sapling anchors are the roots of the Sprout and Sapling note commitment trees that have already been calculated for the last transaction(s) in the block that have JoinSplits in the Sprout case and/or Spend/Output descriptions in the Sapling case. These should be passed as fields in the Commit*Block requests.

Due to the coinbase maturity rules, the Sprout root is the empty root for the first 100 blocks. (These rules are already implemented in contextual validation and the anchor calculations.) Therefore, zcashd's genesis bug is irrelevant for the mainnet and testnet chains.

Hypothetically, if Sapling were activated from genesis, the specification requires a Sapling anchor, but zcashd would ignore that anchor.

These updates can be performed in a batch or without necessarily iterating over all transactions, if the data is available by other means; they're specified this way for clarity.

Accessing previous blocks for contextual validation

The state service performs contextual validation of blocks received via the CommitBlock request. Since CommitBlock is synchronous, contextual validation must also be performed synchronously.

The relevant chain for a block starts at its previous block, and follows the chain of previous blocks back to the genesis block.

Relevant chain iterator

The relevant chain can be retrieved from the state service as follows:

  • if the previous block is the finalized tip:
    • get recent blocks from the finalized state
  • if the previous block is in the non-finalized state:
    • get recent blocks from the relevant chain, then
    • get recent blocks from the finalized state, if required

The relevant chain can start at any non-finalized block, or at the finalized tip.

Relevant chain implementation

The relevant chain is implemented as a StateService iterator, which returns Arc<Block>s.

The chain iterator implements ExactSizeIterator, so Zebra can efficiently assert that the relevant chain contains enough blocks to perform each contextual validation check.

fn main() {
impl StateService {
    /// Return an iterator over the relevant chain of the block identified by
    /// `hash`.
    /// The block identified by `hash` is included in the chain of blocks yielded
    /// by the iterator.
    pub fn chain(&self, hash: block::Hash) -> Iter<'_> { ... }

impl Iterator for Iter<'_>  {
    type Item = Arc<Block>;
impl ExactSizeIterator for Iter<'_> { ... }
impl FusedIterator for Iter<'_> {}

For further details, see PR 1271.

Request / Response API

The state API is provided by a pair of Request/Response enums. Each Request variant corresponds to particular Response variants, and it's fine (and encouraged) for caller code to unwrap the expected variants with unreachable! on the unexpected variants. This is slightly inconvenient but it means that we have a unified state interface with unified backpressure.

This API includes both write and read calls. Spotting Commit requests in code review should not be a problem, but in the future, if we need to restrict access to write calls, we could implement a wrapper service that rejects these, and export "read" and "write" frontends to the same inner service.


fn main() {
CommitBlock {
    block: Arc<Block>,
    sprout_anchor: sprout::tree::Root,
    sapling_anchor: sapling::tree::Root,

Performs contextual validation of the given block, committing it to the state if successful. Returns Response::Added(block::Hash) with the hash of the newly committed block or an error.


fn main() {
CommitFinalizedBlock {
    block: Arc<Block>,
    sprout_anchor: sprout::tree::Root,
    sapling_anchor: sapling::tree::Root,

Commits a finalized block to the rocksdb state, skipping contextual validation. This is exposed for use in checkpointing, which produces in-order finalized blocks. Returns Response::Added(block::Hash) with the hash of the committed block if successful.


Computes the depth in the best chain of the block identified by the given hash, returning

  • Response::Depth(Some(depth)) if the block is in the best chain;
  • Response::Depth(None) otherwise.

Implemented by querying:

  • (non-finalized) the height_by_hash map in the best chain, and
  • (finalized) the height_by_hash tree


Returns Response::Tip(block::Hash) with the current best chain tip.

Implemented by querying:

  • (non-finalized) the highest height block in the best chain
  • (finalized) the highest height block in the hash_by_height tree, if the non-finalized state is empty


Returns Response::BlockLocator(Vec<block::Hash>) with hashes starting from the current chain tip and reaching backwards towards the genesis block. The first hash is the best chain tip. The last hash is the tip of the finalized portion of the state. If the finalized and non-finalized states are both empty, the block locator is also empty.

This can be used by the sync component to request hashes of subsequent blocks.

Implemented by querying:

  • (non-finalized) the hash_by_height map in the best chain
  • (finalized) the hash_by_height tree.



  • Response::Transaction(Some(Transaction)) if the transaction identified by the given hash is contained in the state;

  • Response::Transaction(None) if the transaction identified by the given hash is not contained in the state.

Implemented by querying:

  • (non-finalized) the tx_by_hash map (to get the block that contains the transaction) of each chain starting with the best chain, and then find block that chain's blocks (to get the block containing the transaction data)
  • (finalized) the tx_by_hash tree (to get the block that contains the transaction) and then block_by_height tree (to get the block containing the transaction data), if the transaction is not in any non-finalized chain



  • Response::Block(Some(Arc<Block>)) if the block identified by the given hash is contained in the state;

  • Response::Block(None) if the block identified by the given hash is not contained in the state;

Implemented by querying:

  • (non-finalized) the height_by_hash of each chain starting with the best chain, then find block that chain's blocks (to get the block data)
  • (finalized) the height_by_hash tree (to get the block height) and then the block_by_height tree (to get the block data), if the block is not in any non-finalized chain



  • Response::Utxo(transparent::Output)

Implemented by querying:

  • (non-finalized) if any Chains contain OutPoint in their created_utxos get the transparent::Output from OutPoint's transaction
  • (finalized) else if OutPoint is in utxos_by_outpoint return the associated transparent::Output.
  • else wait for OutPoint to be created as described in RFC0004


  • Restarts can cause zebrad to redownload up to the last one hundred blocks it verified in the best chain, and potentially some recent side-chain blocks.

  • The service interface puts some extra responsibility on callers to ensure it is used correctly and does not verify the usage is correct at compile time.

  • the service API is verbose and requires manually unwrapping enums

  • We do not handle reorgs the same way zcashd does, and could in theory need to delete our entire on disk state and resync the chain in some pathological reorg cases.

  • testnet rollbacks are infrequent, but possible, due to bugs in testnet releases. Each testnet rollback will require additional state service code.


  ┌───────────┐     ┌───────────┐     ┌───────────┐     ┌───────────┐
  │PeerServer │     │PeerServer │     │PeerServer │     │PeerServer │
  │ ┌───────┐ │     │ ┌───────┐ │     │ ┌───────┐ │     │ ┌───────┐ │
  │ │┌─────┐│ │     │ │┌─────┐│ │     │ │┌─────┐│ │     │ │┌─────┐│ │
  │ ││ Tcp ││ │     │ ││ Tcp ││ │     │ ││ Tcp ││ │     │ ││ Tcp ││ │
  │ │└─────┘│ │     │ │└─────┘│ │     │ │└─────┘│ │     │ │└─────┘│ │
  │ │Framed │ │     │ │Framed │ │     │ │Framed │ │     │ │Framed │ │
  │ │Stream │ │     │ │Stream │ │     │ │Stream │ │     │ │Stream │ │
  │ └───────┘─┼─┐   │ └───────┘─┼─┐   │ └───────┘─┼─┐   │ └───────┘─┼─┐
┏▶│     ┃     │ │ ┏▶│     ┃     │ │ ┏▶│     ┃     │ │ ┏▶│     ┃     │ │
┃ │     ┃     │ │ ┃ │     ┃     │ │ ┃ │     ┃     │ │ ┃ │     ┃     │ │
┃ │     ▼     │ │ ┃ │     ▼     │ │ ┃ │     ▼     │ │ ┃ │     ▼     │ │
┃ │ ┌───────┐ │ │ ┃ │ ┌───────┐ │ │ ┃ │ ┌───────┐ │ │ ┃ │ ┌───────┐ │ │
┃ │ │ Tower │ │ │ ┃ │ │ Tower │ │ │ ┃ │ │ Tower │ │ │ ┃ │ │ Tower │ │ │
┃ │ │Buffer │ │ │ ┃ │ │Buffer │ │ │ ┃ │ │Buffer │ │ │ ┃ │ │Buffer │ │ │
┃ │ └───────┘ │ │ ┃ │ └───────┘ │ │ ┃ │ └───────┘ │ │ ┃ │ └───────┘ │ │
┃ │     ┃     │ │ ┃ │     ┃     │ │ ┃ │     ┃     │ │ ┃ │     ┃     │ │
┃ └─────╋─────┘ │ ┃ └─────╋─────┘ │ ┃ └─────╋─────┘ │ ┃ └─────╋─────┘ │
┃       ┃       └─╋───────╋───────┴─╋───────╋───────┴─╋───────╋───────┴───────┐
┃       ┃         ┃       ┃         ┃       ┃         ┃       ┃               │
┃       ┃         ┃       ┃         ┃       ┃         ┃       ┃               │
┃       ┗━━━━━━━━━╋━━━━━━━┻━━━━━━━━━╋━━━━━━━┻━━━━━━━━━╋━━━━━━━┻━━━━━━━━━┓     │
┗━━━━━━━┓         ┗━━━━━━━┓         ┗━━━━━━━┓         ┗━━━━━━━┓         ┃     │
 ┌──────╋─────────────────╋─────────────────╋─────────────────╋──────┐  ┃     │
 │      ┃                 ┃                 ┃                 ┃      │  ┃     │
 │┌───────────┐     ┌───────────┐     ┌───────────┐     ┌───────────┐│  ┃     │
 ││PeerClient │     │PeerClient │     │PeerClient │     │PeerClient ││  ┃     │
 │└───────────┘     └───────────┘     └───────────┘     └───────────┘│  ┃     │
 │                                                                   │  ┃     │
 │┌──────┐      ┌──────────────┐                                     │  ┃     │
 ││ load │      │peer discovery│                              PeerSet│  ┃     │
 ││signal│   ┏━▶│   receiver   │          req: Request, rsp: Response│  ┃     │
 │└──────┘   ┃  └──────────────┘         routes all outgoing requests│  ┃     │
 │    ┃      ┃                               adds peers via discovery│  ┃     │
 └────╋──────╋───────────────────────────────────────────────────────┘  ┃     │
      ┃      ┃                                             ▲            ┃     │
      ┃      ┣━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓             ┃            ┃     │
      ┃      ┃     ┏━━━━━━━━━━━╋━━━━━━━━━━━━━╋━━━━━━━━━━━━━┫            ┃     │
      ▼      ┃     ┃           ┃             ┃             ┃            ┃     │
  ┌────────────────╋───┐┌────────────┐┌─────────────┐      ┃            ┃     │
  │Crawler         ┃   ││  Listener  ││Initial Peers│      ┃            ┃     │
  │            ┌──────┐││            ││             │      ┃            ┃     │
  │            │Tower │││            ││             │      ┃            ┃     │
  │            │Buffer│││listens for ││ connects on │      ┃            ┃     │
  │            └──────┘││  incoming  ││  launch to  │      ┃            ┃     │
  │uses peerset to     ││connections,││ seed peers  │      ┃            ┃     │
  │crawl network,      ││   sends    ││specified in │      ┃            ┃     │
  │maintains candidate ││ handshakes ││ config file │      ┃            ┃     │
  │peer set, connects  ││  to peer   ││  to build   │      ┃            ┃     │
  │to new peers on load││ discovery  ││initial peer │      ┃            ┃     │
  │signal or timer     ││  receiver  ││     set     │      ┃            ┃     │
  └────────────────────┘└────────────┘└─────────────┘      ┃            ┃     │
             │        zebra-network internals              ┃            ┃     │
─ ─ ─ ─ ─ ─ ─│─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┃─ ─ ─ ─ ─ ─ ╋ ─ ─ ┼
             │              exposed api                    ┃            ┃     │
             │             ┌────────────────────────┐      ┃            ┃     │
             │             │Arc<Mutex<AddressBook>> │      ┃            ┃     │
             │             │last-seen timestamps for│      ┃            ┃     │
             └─────────────│ each peer, obtained by │◀─────╋────────────╋─────┘
                           │ hooking into incoming  │      ┃            ┃
                           │    message streams     │      ┃            ┃
                           └────────────────────────┘      ┃            ▼
                                             │Outbound Service││Inbound Service│
                                             │ req: Request,  ││ req: Request, │
                                             │ rsp: Response  ││ rsp: Response │
                                             │                ││               │
                                             │  Tower Buffer  ││  routes all   │
                                             └────────────────┘│   incoming    │
                                                               │requests, uses │
                                                               │   load-shed   │
                                                               │ middleware to │
                                                               │ remove peers  │
                                                               │ when internal │
                                                               │ services are  │
                                                               │  overloaded   │


zebra-checkpoints uses a local zcashd instance to generate a list of checkpoints for Zebra's checkpoint verifier.

Developers should run this tool every few months to add new checkpoints for the checkpoint_sync = true mode. (By default, Zebra syncs up to Canopy using checkpoints. These checkpoints don't need to be updated.)

For more information on how to run this program visit Zebra checkpoints document

API Reference

Zebra's API documentation is generated using Rustdoc: