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Exonum Data Model

This page describes Exonum data storage principles, from the database engine used (LevelDB), to the abstractions that are used in client applications.

  1. Exonum table types lists supported types of data storage collections. Tables represent the highest abstraction level for data storage
  2. Low-level storage explains how tables are persisted using LevelDB
  3. View layer describes the wrapper over the DB engine that ensures atomicity of blocks and transactions
  4. List of system tables contains tables used directly by the Exonum core
  5. Indexing gives an insight how indexes over structured data can be built in Exonum

Table Types

Tables (aka indexes) perform the same role as in relational database management systems (RDBMSs). Every table stores records of a specific type. However, unlike RDBMS tables, all Exonum tables internally are implemented as wrappers around key-value stores. Both keys and values in the wrapped stores are persisted as byte sequences. Exonum does not natively support operations (matching, grouping, sorting, etc.) over separate value fields, as it is the case with other key-value storages.

Key Sorting and Iterators

Exonum tables implement iterators over stored items (or keys, values, and key-value pairs in the case of maps). Such iterators use key ordering of the underlying key-value storage to determine the iteration order. Namely, keys are lexicographically ordered over their binary serializations; this ordering coincides with that used in LevelDB.


BaseIndex represents the most basic table type. Other table types wrap BaseIndex, enhancing its functionality for specific use cases. BaseIndex implements a map interface:

  • Get, set and remove value by key
  • Check if the specific key presents
  • Iterate over the key-value pairs in the lexicographic key order
  • Clear the table (i.e., remove all stored key-value pairs)


BaseIndex should not be used directly. Rather, you should use a built-in table type that wraps BaseIndex, or write your own.


MapIndex implements a key-value store aka a map. It has the following functionality:

  • Get, set and remove value by key
  • Check if a specific key is present in the map
  • Iterate over the key-value pairs in the lexicographic key order
  • Iterate over keys in the lexicographic key order
  • Iterate over values in the lexicographic key order
  • Clear the map (i.e., remove all stored key-value pairs)


ListIndex represents an array list. The following operations are supported:

  • Get and set a list item by index
  • Append an item to the list
  • Pop or poll the last item from the list
  • Get the list length
  • Check if the list is empty
  • Iterate over index-item pairs ordered by index
  • Insert a sequence of items from an iterator
  • Truncate the list to the specified length
  • Clear the list (i.e., remove all stored items from the list)

ListIndex does not support inserting items in the middle of the list or removing items by index (although it is still possible to implement these operations manually).

Implementation Details

ListIndex saves its items to the internal BaseIndex map with 8-byte unsigned item indexes as keys, serialized in big-endian form (to support proper iteration). The list length is saved in this map with a zero-length byte sequence as the key.


ValueSetIndex implements a hash set. The following operations are implemented:

  • Add and remove set elements
  • Check if an element is already present using the element itself or its hash
  • Iterate over stored elements in the lexicographic order of their hashes
  • Iterate over hashes of elements in the lexicographic order
  • Clear the set (i.e., remove all elements)

The hash used in ValueSetIndex is calculated using the hash() method of the StorageValue trait. All built-in types implementing StorageValue compute this hash as SHA-256 of the binary serialization of a type instance.

Implementation Details

Internally, ValueSetIndex uses BaseIndex with element hashes as keys, and elements themselves as corresponding values.


KeySetIndex implements a set. The following procedures are implemented:

  • Add and remove set elements
  • Check if a specific element is in the set
  • Iterate over elements in the lexicographic order
  • Clear the set (i.e., remove all stored elements)

Implementation Details

Internally, set elements are inserted to the underlying BaseIndex as (&element, ()) (i.e., the element is used as a key, and the value is always empty).

KeySetIndex vs ValueSetIndex

While ValueSetIndex uses a hash as a key for the underlying BaseIndex, KeySetIndex puts an entire binary serialization of an element into the key.

  • KeySetIndex does not have an additional overhead on hashing set elements.
  • KeySetIndex should not be used when the set elements are relatively big; only small elements should be stored in it (such as integers, small strings, small tuples). On the other hand, the ValueSetIndex more easily handles storing big and complex elements.
  • The KeySetIndex introduces a lexicographical order over stored elements, while the ValueSetIndex order elements arbitrarily due to hash function properties.

Merklized Indexes

Merklized indexes represent a list and map with additional features. Such indexes can create the proofs of existence or absence for stored data items.

When a light client requests data from an Exonum full node, the proof can be built and sent along with the actual data. Having block headers and this proof, the client may check that received data was really authorized by the validators without having to replicate the entire blockchain contents.


ProofListIndex implements a Merkle tree, which is a Merklized version of an array list. It implements the same methods as ListIndex, and adds an additional feature: based on Merkle trees, ProofListIndex allows efficiently creating compact proofs of existence for the list items. The following additional procedures are implemented:

  • Get the height of the Merkle tree. As the tree is balanced (though may be not full), its height is close to log2 of the list length
  • Get the value of the tree root (i.e., the hash of the entire Merkle tree)
  • Build a proof of existence for an item at a specific position
  • Build a proof of existence for items at a specific contiguous index range


ProofListIndex is append-only; it does not allow deleting list items. The only way to delete an item from a ProofListIndex is clearing it.

Implementation Details

As with ListIndex, list items are stored with 8-byte keys. However, ProofListIndex also persists all intermediate nodes of the Merkle tree built on top of the list, in order to quickly build proofs and recalculate the Merkle tree after operations on the list.


ProofMapIndex is a Merklized version of a map based on Merkle Patricia tree. It implements the same methods as the MapIndex, adding the ability to create proofs of existence for its key-value pairs, or proofs of absence if a key is absent in the map. The following additional procedures are supported:

  • Get the root node’s value
  • Build a proof for the requested key. Tree proves either key existence (and its value), or key absence

Low-level Storage

Exonum uses third-party database engines to persist blockchain state locally. To use the particular database, a minimal Database interface should be implemented for it:

  • Get value by key
  • Put new value at the key (insert or update the saved one)
  • Delete key-value pair by key

All the tables functionality is reduced to these atomic call types.

As of Exonum 0.1, LevelDB is used as the database engine. RocksDB support is planned.

All the values from different tables are stored in one big key-value table at the low-level storage, wherein the keys are represented as a byte sequence, and values are serialized according to Exonum binary serialization format. Keys of the wrapped BaseIndex of a specific table are mapped to the low-level storage keys in a deterministic manner using table identifiers.

Table Identifiers

Every table is uniquely identified by the compound prefix, which is used to map table keys into keys of the underlying low-level storage. The keys are prepended with this prefix which is unique to each table, thus allows to distinguish values from different tables.

The table prefix consists of the service ID and an internal identifier inside the service. All tables created with the same prefix will be the views of the same data.

Services identifier is a 2-byte unsigned integer, u16. System tables have service ID equal to 0. Tables inside services are identified with u8 integers and an optional suffix. If the suffix is present, the u8 integer denotes a group of tables, rather than a single table, and suffixes are used to distinguish tables within the group.


Key key at the table named BTC (0x42 0x54 0x43 in ASCII) at the table group 0x03 for the service with ID 0x00 0x01 matches the following key in the LevelDB map:

0x00 0x01 | 0x03 | 0x42 0x54 0x43 | key

Here, | separates logical components of the low-level key.

It is advised to use a gen_prefix function for creating table prefixes. See the schema of Exonum core for an example.


Table identifiers can also be created manually, but it could be risky. It is strongly advised not to admit a situation when one table identifier inside the service is a prefix for another table in the same service. Such cases may cause unpredictable collisions between logically different keys and elements.

View Layer

Exonum introduces additional layer over database to handle transaction and block atomicity.


Patch is a set of serial changes that should be applied to the low-level storage atomically. A patch may include two types of operations: put a value addressed by a key, or delete a value by a key.


Snapshot fixes the storage state at the moment of snapshot creation and provides a read-only API to it. Even if the storage state is updated, the snapshot still refers to the old table content.


Forks implement the same interfaces as the database underneath, transparently wrapping the real data storage state, and some additional changes. Every fork is based on the storage snapshot. From the outer point of view, the changes are eagerly applied to the data storage; however, these changes are stored directly in the fork and may be easily rolled back. Moreover, there may be different forks of the same database snapshot.

Forks are used during transaction and block processing. A fork is successively passed to each transaction in the block to accumulate changes produced by the transactions, in a patch. If one of transactions in the block quits with an unhandled exception (i.e., raises panic) during execution, its changes are promptly rolled back, so that execution of the following transactions continues normally.

System Tables

The core maintains tables that are used for core blockchain functionality:

  • transactions: MapIndex
    Represents a map from transaction hash into raw transaction structure.
  • tx_location_by_hash: MapIndex
    Keeps the block height and tx position inside block for every transaction hash.
  • blocks: MapIndex
    Stores block object for every block height.
  • block_hashes_by_height: ListIndex
    Saves a block hash that has the requested height.
  • block_txs: ProofListIndex
    Group of tables keyed by the block height. Each table keeps a list of transactions for the specific block.
  • precommits: ListIndex
    Group of tables keyed by the block hash. Each table stores a list of validators’ precommits for the specific block.
  • configs: ProofMapIndex
    Stores the configurations content in JSON format, using its hash as a key.
  • configs_actual_from: ListIndex
    Builds an index to get a configuration activating at a specific height quickly.


Unlike relational databases, Exonum does not support indices over fields of table elements as an first-class entity. However, it is possible to create additional tables with indexing semantics and update their content together with the tables being indexed.


The system table block_txs stores a list of transactions for every block. tx_location_by_hash is an auxiliary table that provides an index to quickly lookup block_txs by a transaction hash.