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Transwarp TimeLyre
Distributed Time Series Database
Support tens of millions-level real-time writing, precise time series query
Product Introduction
Transwarp TimeLyre is an enterprise-level distributed time series database developed by Transwarp Technology. The product is designed and implemented based on Transwarp's big data products ecology. Timelyre has the characteristics of high-throughput real-time writing, accurate time series query, and ultra-high data compression rate, which can effectively support various time series data business scenarios such as IoT, energy manufacturing, and financial quantitative trading.
Four Reasons to Choose TimeLyre
High-performance multi-protocol insertion
TimeLyre supports multiple data writing methods, such as in real-time and batch.In real-time writing mode, Timelyre supports multi-concurrency and insertion of tens of millions of data points per second, which can ensure the timeliness of data retrieval, and is an excellent choice for enterprises to build real-time data warehouses. In addition, TimeLyre supports storage through SQL, file loading, API, and various industrial IoT communication protocols, which can meet the requirements of various complex business scenarios.
Time series data retrieval
TimeLyre adopts columnar storage, built-in multiple index structures, and the latency of time series data retrieval is controlled in milliseconds.TimeLyre has great advantages in time range-based retrieval and statistical analysis scenarios.
Distributed time series high compression storage
TimeLyre has a super-high data compression rate. Lossless data compression supports a variety of data types, encoding methods, and compression algorithms, and the data compression rate can reach 5-20 times; Transwarp Technology can provide solutions for lossy data compression, and the data will be stored within a certain precision, which further improves the compression ratio. The super-high data compression rate will effectively save hardware costs for enterprises.
Multi-model complex data SQL analysis
TimeLyre uses a distributed vectorized computing engine to support the computing and analysis of massive time series data with standard SQL, covering multiple complex SQL analysis functions such as association query, aggregation query, and nested query. And Transwarp Technology provides solutions for complex analysis such as simultaneous correlation of different data models. Multi-modal analysis can effectively break the barriers of data storage management and realize unified management and data fusion of business data.
Application Scenarios
Industrial IoT
Industrial enterprises such as energy and manufacturing widely use sensors to collect production data. IoT data is time-series and the collection frequency of sensors is very high, IoT time-series data presents the characteristics of mass, correlation, timeliness, and real-time. TimeLyre supports the simultaneous storage of massive equipment measurement point data. Real-time stream data import can support tens of millions of data points per second, which can perfectly meet the data storage requirements of industrial scenarios.
Smart Wear
As one of the fastest-growing industries in recent years, the smart wear industry is characterized by periodic statistical analysis of heartbeat, body temperature, and movement.TimeLyre provides millisecond-level time series retrieval, and also supports massive data analysis of time series data itself, such as trend analysis, data statistics, etc., which perfectly meets the data retrieval requirements in the field of smart wear.
Financial Quantification
Financial industry has large-scale high-frequency access transactions every day. Due to the huge amount of time series data, it has high requirements for writing and analysis performance, real-time performance, and scalability.Timelyre supports streaming insertion of massive time series data and high-speed retrieval of time series data. TimeLyre uses a distributed architecture, which can scale horizontally and provide scalable solutions for data consistency and multi-replication disaster recovery.
Customer Cases
  • An energy company
  • A manufacturing company
Customer needs
○ Massive time series data high-throughput insertion, with a unified big data platform for processing
Solutions
○ Use TimeLyre to solve high-throughput streaming insertion of massive measuring points, provide high compression rate and save storage cost, and SQL query support flexibile business expansion.
Project results
Successfully completed the construction of an application platform based on time series database, which can meet the real-time storage and analysis of massive point data at the same time. And the high compression rate effectively saves hardware costs.

Transwarp, Shaping the Future Data World