Know the disruptive technology running into LeanXcale

LeanXcale can offer an incredible set of functionalities thanks to a never seen technology, as a result of 20 years of investigation of some of the most brilliant minds in the database field, leading by the former UPM professor Ricardo Jimenez-Peris.

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Linear Scalability

Run on a PC or in 100s of servers, get always a linear performance. Process up to millions of transactions per second. Store up to petabytes. Query up to billions of rows instantly, thanks to Iguazu Tech.


Hybrid Transactional Analytical Processing

Real time analytics: analytics queries directly from operational data. No ETL, no data movements, great performance.

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High Availability

A novel active-active replication algorithm: No more updates lost nor downtime due to passive replication. No more bottlenecks nor unacceptable costs due to obsolete active replication.


Non-Intrusive Elasticity

Our novel non-intrusive data migration algorithm allows to move data while is being updated and keeping full ACID consistency and without disrupting operations. Grow or shrink according to current needs with zero downtime. Minimizes operational costs by reducing HW resources to actual needs

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Dual SQL and Key-Value Interface

One database two interfaces: Key-value for ultra-efficiency and semistructured data, SQL for simplicity and query-power.


Ultra-Efficient Storage Engine

Designed to work efficiently in multi-core and many-core HW. Ultra-NUMA efficient.

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Polyglot Queries

View and join information among all databases portfolio. LeanXcale can query MongoDB, HBase, Neo4J and your RDBMS in their native API in a single view combining the information to create a single resultset.


Integration with Data Lakes

When defining metadata and parsing of HDFS files, they become read-only SQL tables. SQL queries can query and correlate operational data and data lakes.

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Online Aggregations

Compute parrallel aggregations in insertion time without conflicts. Aggregate analytical queries become costless single row queries.


Efficient for Range Queries & Random Updates:

As efficient as B+-trees for range queries (used by relational DBs). As efficient as LSM-trees for random updates/inserts (used by key-value data stores).

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Vectorial Processing

Hardware acceleration exploiting SIMD instructions. Acceleration of scans with predicates. Acceleration of aggregation. Acceleration of sorting


Efficient Distributed Secondary Indexes

They scale as distributed indexes. They are as efficient as centralized indexes.

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Do you have any doubts? Do you want to request enhancement?