InfinityDB Server is a complete secure user-friendly web-based DBMS. Software clients or users securely access dynamic custom REST APIs through PatternQueries to conceal the flexible evolving schema. There is no coding, configuration editing, command-line work, or downtime. PatternQueries are like complete mini-applications but are concise, declarative, and readable, while operating on images, documents and nestable tabular structures with 12 data types. Edit anything in graphical, JSON, i-code, or CSV formats. Collaborate or backup on remote servers. Based on InfinityDB Embedded.
InfinityDB Embedded and InfinityDB Encrypted are Java components for use in Java applications, licensable separately directly from Boiler Bay. They have superior performance, multi-core concurrency, compression, transaction support, safety and near indestructability. Authenticate and encrypt 100% of the data 100% of the time with AES256, HMAC-SHA256, X509 signatures and key rotation. The API and data model are very simple. Each db is a single file so administration is easy. Many custom uses.
With the secure server, you effortlessly run queries or create and maintain data, schemas, REST Microservices APIs, queries, and indexing via the web to enhance productivity without the need for coding or configuration file editing, even for administration. Tuning, testing, exploration and experimentation use live edits to data and queries, without any server downtime or interruption.
PatternQueries go far beyond SQL; they almost resemble programs but remain declarative rather than algorithmic. They are clear, concise, easy to read and learn, and editable as text or graphics. Each REST access URL corresponds to a single query, serving as a secure interface that conceals the database structure – nothing else is necessary. Queries are stored as structured data within their respective databases, travelling with the data and protecting it.
Almost anything can easily be connected via tiny to elaborate APIs, as REST is a simple protocol supported by virtually all client languages. The server runs either on-premises or in a customer-launched Amazon AWS EC2 instance based on an AMI.
All Data Types
Images, pdfs or any other file or binary data (Blobs), documents, tables, tuples, sets, and lists may be flexibly nested, with 12 data element types. Metadata ‘class’ and ‘attribute’ data types are seamlessly embedded within the data, allowing for instant and incremental extensions. All data is more than just JSON documents as in other NoSQL DBMS; it is strongly typed, efficiently sorted, internally binary encoded for speed and compression, and interchangeable with any format on demand. The richness of the structures falls out from the expressiveness of the simple data model – spend 30 seconds to learn it. Blobs are simply broken down into smaller manageable units which then blend into the rest of the data.
All of the data displays are just alternative views of a simple single underlying data model. Each view, data manipulation operation or data communication method applies to any data. Once the elementary data model is learned, all else falls in place.
Data exploration and editing are available in both graphical and text modes including JSON, CSV, and i-code. Graphically, data displays as a nestable mix of images, pdfs, or any other files or blob data, text, tables, documents and structures. Data is rich, flexible, informal or formalized, yet intuitive. PatternQueries can provide further aspects by transforming any data structure. See the Server for more details.
A Tidy App
A PatternQuery is like a tidy little server-side application, while with SQL, there must also be complex, fixed, ugly algorithmic ‘glue’ code in the server to adapt to particular uses. The glue typically concatenates strings into SQL statements, executes, interprets result rows, and repeats this to assemble the output – an inefficient, error-prone, insecure, tedious mess. In contrast, a PatternQuery user or client application sees one formalized request and response. Requests and responses may be tiny or elaborate, small or large, either efficient binary blobs or JSON. They are immune to problems like SQL injection attacks.
A PatternQuery effectively bundles any mixture of kinds of database operations, DML, DDL, and computation. Features include reducers, select, project, join, pattern matching, expressions, sorting, set operations, incremental schema evolution, indexing or ‘replication’, tree-like structure nesting, and much more. However, this richness is not a complication from the query author’s perspective because these are mostly available as a consequence of the simplicity of the data model, and there is no need to memorize specific language features for these concepts. A query is a pattern tree of literals expressions and symbols and a result tree of literals expressions and symbols, where the symbols can have descriptive attributes.
The PatternQuery compiler is highly optimized, and while it offers optional fanout hints, it always completes the compilation phase within milliseconds, unlike traditional RDBMS systems. Query authors can customize queries to effectively embed optimal execution plans, such as the choice of search paths and joins of multiple replications or inversions. A unique ‘zig-zag’ algorithm significantly accelerates joins, intersections, and differences, all without requiring temporary storage, even in the absence of inversions. The pattern tree determines an efficient navigation of the inherently sorted data rather than a filtered scan. The underlying InfinityDB Embedded ‘database engine’ is highly optimized as well, and is multi-core threaded for speed and isolation of short and long operations.
Unlike hand-crafted REST interfaces and web servers, InfinityDB is secured by SSL/TLS for all communications. Administrative data is encrypted and passwords hashed. The admin creates users, roles, databases, permissions and grants, and the admin sets passwords. PatternQuery execute permissions are finely controllable via named interfaces, with queries optionally declared as ‘setters’, ‘getters’ or both. Permissions can be selective on these designations and on interface names or prefixes. Virtually all security and database administration are administered via the web, so there are no configuration files or server downtime, and management is simple and clear.
When eventually the X509 certificate is to be installed, after a domain name is registered, it needs special attention through the root terminal, as with almost any SSL/TLS system, for a one-time setup and occasional certificate renewal. We provide a custom certificate-tool.py to simplify handling self-signed, regular, or free 90-day lets-encrypt certificates. This tool drives OpenSSL for you (whew!) or let’s-encrypt. It does not yet auto-renew the let’s encrypt certificates, but Let’s-encrypt will email you when needed. The server must be rebooted and will take a few seconds to find the new certificate.
Manual backups can use remote databases or Amazon EC2 snapshots for online interrupt-free use. If the root terminal is available, file or directory copies, or rsync can be used, but the server must be stopped manually as databases are locked. Each database is a single file, so management is not complex. The snapshot route will create a point-in-time copy, which is compatible with the consistency-control mechanism of InfinityDB that carefully uses fsync.
There is no fixed separation between ‘key’ and ‘value’ as in RDBMS, distributed hash tables or ‘document’ databases. Instead, all data is kept sorted and is randomly or sequentially searchable quickly, at all scales of granularity at all times, based on arbitrary prefixes of any length. A search prefix is like a variable-length composite key. All operations, queries, display, editing, and so on are symmetrical and scale-independent, so there is no special handling of 10 items or 10^12 Items for individual prefix-based retrievals, which take from a microsecond to a few milliseconds, even for updates. Hence it is useful to use the nesting capability to allow access with prefixes of various lengths and patterns at different times. This can be augmented with the unique ‘zigzag’ PatternQuery algorithm (see the examples).
Replications and Inversions
Large databases can benefit from ‘replications’ or ‘inversions’ of data in which different prefix structures can be used to keep access fast and avoid long sequential scans. PatternQueries explicitly maintain such replications, providing an opportunity for the author to do custom tuning and to get optimal performance. A replication is often just a permutation of the variable parts of multiple data Items, and an inversion – like an RDBMS index – is just a subcase with two variable parts. Novel replication architectures can be invented for dedicated purposes, such as an actual genome database that provides retrieval of genes based on ranges of DNA positions out of billions. Beyond simple permutations, replications can also include computations.
Remote servers and InfinityDB Embedded Java clients can be connected via a binary ‘ItemPacket’ protocol for consolidation, backup, curation, and collaborative purposes. The Server can be configured to use this to make databases that appear to be local actually be remote transparently to users and client software.
Custom architectures using this facility can be used in a wide range of distributed systems. The interface is simple, because it just implements the basic ‘ItemSpace‘ data model and API with almost no special code.
For more see InfinityDB Server where we show the web user interface and explain the data model and PatternQueries.
- Data Science using the server’s text or graphical database browser and PatternQueries
- Collaborative work involving people sharing both local and remote distributed data including images, files, tables, and nested data
- IoT or Microservices networks benefiting from PatternQueries defining the REST APIs
- Distributed database architectures enabled by the server’s ItemPacket protocol
- High performance Java applications utilizing the low-level ItemSpace and Map APIs with encryption.
Release of the server on AWS MarketPlace is pending. Availability on AWS EC2 is custom until then – email firstname.lastname@example.org.
The InfinityDB Server version utilizes the architecture depicted below. It is designed for seamless deployment on Amazon Elastic Computing Cloud (EC2), allowing for instant launch as a virtual machine. Additionally, it can be executed on any platform where Java is supported with an ‘on-premises’ license. On the right-hand side, you can see the REST clients, which can encompass various devices such as computers, laptops, desktops, mobile devices, or IoT devices. The REST protocol is widely adopted and extremely user-friendly, making it accessible for any client program. A REST request functions like a remote procedure call, with each API method being handled by a straightforward declarative PatternQuery.
The remote servers represent other InfinityDB instances that have been configured as remotes within the referring server. From the perspective of clients, these remotes appear as regular databases. On the left side, you’ll find the web console, which serves as the interface for administration and interactive data access and querying. All communication is robustly secured through SSL/TLS (Secure Sockets Layer) encryption.
Within the EC2 instance, there are two Elastic Block Store (EBS) volumes: one serves as the root volume for the code, while the other is designated for data storage. The root volume is periodically updated to accommodate newer versions. When operating outside the EC2 environment, the use of two separate volumes is not required.
For more see InfinityDB Server where we show the web user interface and explain the data model and PatternQueries.
InfinityDB Embedded: The Underlying Database ‘Engine’
InfinityDB Embedded and InfinityDB Encrypted are optionally separately available from the server, and they comprise the ‘engine’ that underlies InfinityDB Server to provide the highest available performance, according to our customers and the provided performance tests. Many applications use these for custom purposes because of the flexibility and performance.
- More than 1M ops/sec are typical for multi-threaded insert, delete, and next in cache
- Multi-core overlapping operations scale almost linearly in thread count
- Almost all cores are used with many threads
- Threads use fair scheduling, with very low inter-thread interference
- Random I/O scales logarithmically in file size, with no size limit
- Huge caches are efficient – 1MB to 100GB or more, and are on-heap
- Transactions are fast: 50/s on disk, 300/s on flash, or thousands/sec for delayed durability
- Database open is immediate, even for recovery after abrupt exit
Here are the basic InfinityDB Embedded and InfinityDB Encrypted ‘Database Engine’ features. InfinityDB Encrypted is identical to InfinityDB Embedded but encrypts 100% of the database 100% of the time.
- Each standalone database is in a single file, used by a single JVM. The file format never changes.
- 10 simple API calls (insert, delete, delete suffixes, update, first, next, last, previous, commit, rollback)
- 12 basic data types:
- String, double, float, long, boolean, date, byte array, byte string, char array, index, ‘Class’, ‘Attribute’
- Unlimited nestable data structures and patterns:
- images, files, mime-typed BLOBs, trees, documents, inversions, lists, maps, sets, variable strongly-typed tuples, elegant Entity-Attribute-Value
- Schema is dynamically extensible but can be backwards compatible
- Data compression up to 10x or more, continuous, no memory or storage leaks
- APIs: fast ‘ItemSpace’, fast extended Java -standard ConcurrentNavigableMap adapter
- Zero administration
See the short embedded example code , map access example code, encrypted example code or client/server example code. Also see PatternQuery examples. A dzone.com article shows definitive performance testing with the Java Microbenchmarking Harness.
There are thousands of deployments in current use for years by these large companies and more:
has been shipping InfinityDB Embedded for years to tens of thousands of customers in their successful Crucible and Fisheye repository browser as the foundation for a fast web server, where it gathers and presents repository structure. Atlassian is an Australia-based company that produces collaboration software for software developers.
an international company that uses InfinityDB Embedded in Kuwait in a time-series database for collecting real-time signals from distributed nodes for wellhead health and productivity monitoring. Their system keeps up with a very fast stream of input from hundreds of sensors over radio links, and archives the data for critical later analysis.
is a large Canadian text processing software company that ships InfinityDB Embedded in an enterprise-grade text indexing system. The index provides access to distributed documents based on content.
is an innovative Australian company that uses InfinityDB Encrypted in its ‘ripple-down-rules’ medical data analysis software products. They use encryption for health security compliance.
These companies and more have relied on InfinityDB Embedded for years for critical data storage of their successful commercial products. These projects require extreme speed, zero maintenance, and unique data structuring capabilities. InfinityDB Embedded is licensed for distribution in applications as a jar.
InfinityDB Embedded was already incredibly fast, but then we redesigned it to make use of all cores at the same time, each operating safely on a different thread. Now, InfinityDB Embedded runs at over 1 million ops per second on 8 cores as it scales. You can take advantage of this speed immediately on a server, or you can use multiple threading in your application. Cores are multiplying at Moore’s-law speed, and applications are adding more and more threads.
Without the multi-core technology in InfinityDB Embedded to avoid inter-thread interference, bottlenecks called ‘convoys’ can occur when threads contend for data. Performance can drop dramatically, even far below single-thread speed. The concurrency algorithm is patented now.
InfinityDB Embedded applications can run indefinitely with no DBA attention for installation, management, application upgrade, or schema definitions like create table scripts.
A Database is a Single-File
InfinityDB Embedded uses a single file for each database. The combination of this feature and the instant guaranteed recovery on abrupt application termination help make InfinityDB Embedded administrator free. No logs need to be archived or re-applied. There are no configuration files, temporary files, or text logs. No junk files are left behind after any kind of termination, so there is never any cleanup.
Reliability and Safety
InfinityDB Embedded uses a rugged internal storage update protocol for persistence on demand or cache spilling to disk for large amounts of data that maintains system-wide data integrity, and survives abrupt application termination or other problems. The single data file remains up-to-date, safe, correct, and usable through any event. There is no log-based recovery, hence restart and recovery is immediate in all cases. No unexpected Exceptions are ever thrown: not even due to any kind of deadlock or internal resource limits (optional optimistic locking throws expected Exceptions on conflict however). No dangerous off-heap storage or native libraries are used.
Continuous Space Reclamation
Space allocation for individual and aggregated data is fully dynamic: no space is used until structures are created or after they are deleted. During growing or shrinking, structure storage is always minimal and efficient. The single data file is 100% efficient with compressed data on initial loading, and stays at least 50% efficient in the worst case after very large global transactions, which may include any amount of data. Normally, free space is about 10%. The single file never shrinks. Applications can run forever without gradual space loss. There are no temporary peaks in space usage, or temporary external files. There is no need for occasional reorganization or packing, and there is no garbage collector thread. All freed space is recycled on commit or rollback. Deletions or updates do not leave sparse structures behind – all freed space is reclaimed completely for immediate reuse without rebuilding indexes or running offline reorganizers.
High Compression on Disk and In Memory
InfinityDB Embedded uses continuous, dynamic ZLib and UTF-8 data compression to pack data into variable-length blocks, avoiding almost all wasted space that would normally be needed for internal fragmentation. I/O bandwith is reduced accordingly. Variable-length binary-encoded primitives, variable-length concatenations of primitives or ‘Items’, and prefix and branch-cell suffix compression are used on disk and in the memory cache as well. Data compression means that the branching factor is kept high for fast access, and the OS file cache is better used. For compressible data, 10x is often achieved. There is no pre-allocation or waste in ‘extents’, ‘segments’, ‘clusters’, or fixed-size blocks.No gradual space leaks can occur because free space management is transactional. Any size database benefits from the compression, from 10KB to 100GB and beyond.
Simple NoSQL APIs
There are several APIs, all of which can efficiently transfer images, blobs, or any other data:
- For InfinityDB Embedded and InfinityDB Encrypted:
- ItemSpace – a small proprietary set of methods to query and modify the database at very high speed.
- Map – an extended java.util.concurrent.ConcurrentNavigableMap that wraps the ItemSpace
- JSON any ItemSpace data can be formatted and parsed
- For InfinityDB Server:
- ItemPacket – a proprietary binary protocol for fast Java client to InfinityDB Server remote access, or between Servers.
For the ultimate speed and extreme flexibility, the simple lower-level ‘ItemSpace’ API can do anything. There are only 10 essential storage and retrieval methods that operate on the ItemSpace: insert, delete, deleteSubspace, update, first, next, last, previous, commit, and rollback. You gain low-level access by momentarily allocating a ‘Cu’ cursor, and then using it for the API method invocations and disposing it. There are helper utilities for things like text indexes, hierarchical sorting, inversions, and more. Applications can define rich creative models on top of the ItemSpace. An ItemSpace is like a single sorted set of tuples each being any sequence of components of the 12 primitive data types. For extreme speed, the tuples are actually dealt with as ‘Items’ which use a standard binary encoding for the components up to 1665 chars in length, but the encodings are never directly dealt with.
The nested Map view is a wrapper around the basic ItemSpace API, and it implements and extends the java.util.concurrent.ConcurrentNavigableMap, thereby providing the capability of a ConcurrentHashMap or ConcurrentSkipListMap. InfinityDBMaps may contain other InfinityDBMaps or InfinityDBSets which are extended ConcurrentSets. The InfinityDBMap is a light-weight Object which can be constructed dynamically without itself being persisted: the Map mutator methods actually store data in the ItemSpace database. Extensions to the ConcurrentNavigableMap API include:
- composite keys – variable data types and component count
- composite values or set elements, variable data types and component count
- multi-map – unlimited values per key
- tuple access via variable-length Object arrays
- nestable Maps and Sets
Data in the database can be mapped directly to extended JSON text with a one-to-one correspondence. Utilities for parsing and generating JSON are provided. This goes beyond ‘Document’ databases, because the JSON is not stored as text but instead as compressed ‘paths’ or ‘Items’ each of which represents a JSON value. The 12 data types can be encoded into extended JSON, or ‘underscore-quoted’ standard JSON so any primitive can be a key or value, such as a date, and all keys are sorted. There is no artificial distinction between the ‘container’ of the documents and the documents themselves, so the size of any JSON sub-document depends on only the given path to it – from an entire database down to individual values. Access does not depend on loading and storing entire JSON documents. JSON has been extended to handle blobs such as images or any other file type. A better alternative format is the optional proprietary ‘i-code’ language.
- Global. This persists all current changes to disk, providing Atomic, Consistent, and Durable semantics. It does not use any kind of lock, so it does not provide inter-thread Isolation. However all access is concurrent during the commit by any threads. Effectively, there is a single ‘global transaction’ in effect at all times. Optimistic Locking commits also cause global commits.
- Optimistic. Fine-grained multi-thread transactions use optimistic locking and support complete ACID atomic, consistent, isolated and durable semantics. Locks do not follow the usual rules of other DBMS’ but have the equivalent capability as table locks and row locks, index locks, or even single-column value locks and single set element locks. This diversity of lock types is not actually a complex spectrum of details – it follows trivially from the basic data model and is automatic and almost invisible to the programmer. If desired, the programmer can easily control the lock order for maximum concurrency simply by accessing appropriate data early in the transaction. The locks are actually just set on prefixes of tuples, i.e. prefixes of Items, and are maintained transparently. The set of locked prefixes is kept in memory per database globally and also associated with each thread. Lock conflicts throw an OptimisticLockConflictException and are optionally retried by the application code. Concurrent optimistic transactions can reach hundreds of commits per second on disk, and thousands per second on flash.
Advanced Data Model
Applications do not need to invent binary encodings or convert primitives to binary or text. Data is not stored as formatted text or as custom raw binary, but as an intermediate form, with standard pre-defined binary encodings of the individual Java primitives in a consistent way that allows extremely high speed.
InfinityDB Embedded supports all primitive Java data types and more:
- long (stored as compressed bits to handle byte, short, char, with no more space)
- String (encoded as UTF-16)
- index (for ‘huge sparse arrays’, lists in JSON, and BLOBs/CLOBs, texts)
- short byte arrays up to 1KB (sort by length first, used for BLOBs)
- short UTF-16 char arrays up to 1K chars (sort by length first, used for CLOBs)
- short byte strings up to 1KB (these sort like strings but with bytes instead of 2-byte chars)
- ‘Classes’ metadata identifiers embedded in Items for delimiting other data, describing the schema
- ‘Attributes’ metadata identifiers
Application-Specific Data Models
InfinityDB provides a rich data representation space for structured, semi-structured, or unstructured data. The basic data model is simple but flexible enough to be used by the application to define and represent any mixture of images or other BLOBS, texts or other CLOBS, trees, graphs, key/value multi-maps, sets, documents, text indexes or other inversions, huge sparse arrays, tables with an unlimited number of columns of an unlimited number of values or nested structures per column, Entity-Attribute-Value structures or creative custom structures.
Items and the ItemSpace
All structures in the entire database are represented at the lowest level as a magnitude-ordered set of ‘Items’, where an Item is logically a char array from 0 to 1665 chars long. The simplicity of the low-level format allows great speed and compression. These ordered Items represent the entire state of the database. ItemSpaces come in a wide variety of implementations, but they all have the same simple structure.
In order to make use of the ItemSpace, an Item is formatted as a packed series of ‘components’ of the 12 primitive data types, each of which is variable-length, compressed, explicitly typed, and self-delimiting. So, an Item can be thought of as a tuple with a variable number of elements of any type. The data in the components is formatted such that the sorting is appropriate: raw floats or other types would not sort properly. The internal binary encoding is done by InfinityDB Embedded in a fixed permanent way for forwards and backwards compatibility.
All basic access to the database uses a temporary ‘Cu’ cursor containing one Item and no other state. The binary encoding of each component in an Item is unimportant to the application, which uses only Java primitives indirectly to build and examine Items in a Cu cursor.
Prefixes of Items are often used to logically nest Items into arbitrary recursive sub-spaces, i.e. sets of suffixes. Items can be used to represent sets of fixed-length tuples, the equivalent of CSV files or tables, or they can represent paths to JSON terminal values. JSON can be parsed and formatted from the Items. The JSON is not stored literally: the entire database can be accessed at any level of hierarchical detail, because there is no fixed predefined division between keys and JSON documents. Combinations of tabular, document, blob, or many other structures can be easily intermixed.
Flexible Extensible Data Structures with ‘Class’ and ‘Attribute’ Data Types
If the special Class and Attribute data types are mixed in with the other ‘primitive’ data types in the Items, flexible, ‘incrementally self extending’ structures can be represented. See the InfinityDB Client/Server page for a graphical view of some examples of the flexible structures. An initial Class component is normally used to separate data for unlimited independent uses even without the flexible structuring in a single InfinityDB Embedded file. A Class is encoded as binary but contains a string with an initial capital letter followed by zero or more letters, digits, dot, dash, or underscore (as a regex: [A-Z][A-Za-z0-9._-]*). An Attribute is identical but starts with a lower case letter.
When used to represent a ‘flexible’ tabular structure, keys can be:
- ‘tuples’, where a tuple is any concatenation of zero or more primitives of any type,
- heterogenous – different keys can have different primitive types or tuple types,
- variadic – different keys can be tuples of a different number of primitive types,
- nestable sparse arrays or lists of unlimited size of any key type, i.e. lists, using the ‘index’ data type
Flexible table column values can be the same as keys plus:
- multi-valued, with no limit on number, and where an absence of any value takes no storage,
- CharacterLongObjects or BinaryLongObjects of unlimited size, such as images or files.
Furthermore, any such flexible structures can be nested by concatenating their Items onto the ends of other Items. A particular set of suffixes can contain any kind of nested structure. The ‘Class’ and ‘Attribute’ data type components can represent four patterns depending on their pairings within each Item:
|Class then data then Attribute then data||a ‘table’|
|Class then data then Class then data||a ‘sub-table’|
|Attribute then data then Attribute then data||a ‘sub-attribute’|
|Attribute then data then Class then data||a ‘nested table’|
The GUI display of such flexible structures is very rich – see it in action in InfinityDB Client/Server. Data can also be handled in various character-oriented formats. The displays look like nestable ‘documents’, tables, lists, trees, and so on. Here is a flexible table with Class “Trees”, a multi-value Attribute, ‘composite’ keys of variable component count, and a nested table “Location”.
Forwards and Backwards Schema Compatibility
The ItemSpace model is inherently extensible, but with the flexible ‘Class’ and ‘Attribute’ metadata data types embedded in the Items, databases become ‘self-describing’ and can be extended in ways that avoid incompatibilities with earlier or later database backups, old or new application versions, or changing or extending data producers and consumers like users, Python scripts, bash ‘curl’ commands, or IoT’s or distributed databases.
Virtual View ItemSpaces
InfinityDB Embedded provides many utilities for dynamically viewing one or more underlying ItemSpaces as a virtual ItemSpace . All underlying ItemSpace changes reflect immediately in the virtual view ItemSpace. A view is a true ItemSpace itself:
- ItemSubspace virtually hides and restricts by a fixed prefix of an ItemSpace;
- DeltaItemSpace is a mutable view of a fixed underlying ItemSpace with its own commit and rollback;
- AndSpace views a logically intersected set of underlying ItemSpaces;
- OrSpace views a logically unioned set of underlying ItemSpaces;
- RangeItemSpace views a limited range of Items;
- VolatileItemSpace stores Items in memory non-persistently;
- IncrementalMergingItemSpace views a special kind of index that can be incrementally built and optimized efficiently at any size while +being accessed concurrently. Concurrent deletions are allowed. Text indexing is one use.
Views can be nested. An arbitrarily deep nesting of AndSpace and OrSpace can be flattened automatically for best speed. These capabilities provide a type of instant dynamic query capability without indexes, query compilation, execution, accessor allocation or destruction or temporary space usage. The virtual ItemSpaces are light-weight Objects. Any number of views can exist at once. The views can underlie the Map-based wrappers. They work with the flexible data representation using Class and Attribute data types as well.
See the Manual for detailed information on InfinityDB Embedded (a musty old doc but simple). See Documents (old docs) on the internal structure or the principles for constructing any higher-order data model from the trivial underlying ‘ItemSpace‘ data model. For a Free InfinityDB Embedded Trial Download see the shop. Here is the InfinityDB Embedded_Trial License.
for licensing, email email@example.com, or go to the Amazon Web Services Marketplace (pending as of 2023-10-17).
The AirConcurrentMap Java ConcurrentNavigableMap
A separate product, the fast com.infinitydb.map.AirConcurrentMap API is identical to the Standard Java Maps – in fact it is a java.util.concurrent.ConcurrentNavigableMap, optimized for more than about 1K Entries. You can use our free non-commercial edition or license the commercial edition. Compare the performance with that of the Standard Java Maps. Memory efficiency is higher than any JDK Map as well. Our extensions provide extreme performance for parallel operations even beyond streams. The Map is fast and concurrent due to the same patented technology as is used in InfinityDB.