Most applications start small, a single database, a manageable dataset and few concurrent users. With time and new technological advancements these applications face challenges, surge in traffic, need for concurrent operations and analysis that run in real time rather than on a nightly basis.
Traditional databases like PostgreSQL were not designed to meet these demands at scale. They perform well for smaller workloads, but at enterprise scale they often struggle with concurrency bottlenecks, replication lag, and the overhead of managing both transactions and analytics on the same system. Therefore, the need for a better database management system which helps application scale at ease while maintaining the performance and real time analysis is established.
SingleStore is designed to address these issues. With distributed architecture and universal storage of rowstore and column store structure, it handles both transactional and analytical workloads at scale. In this article we will understand how SingleStore helps in focussing on the most common scaling issues without compromising on the performance.
PostgreSQL's Architectural Strengths and Limits
In the past few years, PostgreSQL has been able to place itself as the first choice of open source databases for building applications. It has been successful in the community and known to many developers because of its strength and its architecture.
PostgreSQL follows the Multi Version Concurrency Control (MVCC) which enables the readers and writer of the transaction data to process without blocking any of the operation. This creates the snapshot isolation ensuring data accuracy and consistency.
Following the basics of ACID properties, the write Ahead Log method ensures the durability of the data and enables recovery in an efficient way.
Its support for major procedural languages enables the development of complex business logic.
Apart from various other advantages that PostgreSQL carries, it allows you to build applications with ease and perform OLTP operations efficiently.
However with all these and other amazing features that it carries, there are few bottlenecks which are hit especially in the case of application scalability. The same architectural decisions that ensure correctness and flexibility, MVCC snapshots, WAL logging, process-based concurrency begin to constrain performance when workloads demand ultra-high concurrency, real-time analytics, or seamless horizontal scale-out.
The Five Critical Scaling Pain points with PostgreSQL
The blog on 5 Signs Your PostgreSQL Database Is Hitting Its Performance Limits very briefly elaborates on the performance bottlenecks faced while the application with PostgreSQL scales. Let’s take it further with real world scenarios which show exactly what happens when your application scales past PostgreSQL’s sweet spot.
An application with a few active users works fine but on a Black Friday sale, when active users spiked, the application built with PostgreSQL crashed. This led to slow website loads, poor sales and an over overwhelmed support team. This happened because of the concurrency bottleneck which happened as PostgreSQL’s process-per-connection model and locking mechanics don’t scale linearly with user growth.
A social media application ran smoothly at moderate scale. It hit the scaling wall when its users were added across regions. A viral post showed up instantly for some users but disappeared for others, depending on which read replica their app hit. PostgreSQL's replication lag across replicas broke under global workload.
A tax software firm scaled PostgreSQL to handle peak tax season with millions of concurrent users. The system worked during the season but for the other eight months, 95% of that expensive infrastructure sat idle. Storage, replicas, and backup costs piled up, while the database couldn’t scale down elastically. The inability to elastic scale and its single node architecture failed the applications and made it inefficient in the season.
A Saas application failed miserably when a heavy workload analytics dashboard was built on top of it. This made the logins fail, causing the analytics board to have inconsistent data and frequent time outs. The inability to handle complex analytics workload together made it fail.
Therefore, while PostgreSQL may work well for small scaled applications, it might get into performance degradation when scaled with larger workloads, operation complexity or any other situation. Hence, it does not make it the first choice of database for an application which demands scalability.
Enterprise requirements demands a new approach
Today’s applications no longer depend on overnight batch processing. They work with real-time data, enabling operational monitoring and customer analytics as events occur. In addition to speed, these applications must provide a low-latency experience, even with large, complex, and unpredictable workloads.
At the same time, companies cannot afford to waste resources. They need flexible systems that can adjust their capacity up and down easily, optimizing costs while maintaining performance during busy times. Beyond flexibility, global businesses require databases that can scale across different regions. These databases must offer fast replication, strong consistency, and reliable failover for users around the world.
For developing enterprise applications, these requirements rarely exist in isolation. They usually come together. For instance, a global SaaS application might need real-time analytics, low-latency interactions, cost efficiency, and multi-region support simultaneously. Meeting these combined needs shows where traditional databases, like PostgreSQL, start to fall short and highlights the need for a new approach.
How does SingleStore solves these challenges
SingleStore is a distributed database built for modern day workloads, which supports real time performance, elastic scalability, hybrid transactions and analytics processing.
SingleStore is built around Universal Storage which combines in-memory rowstore and on-disk columnstore data formats to deliver a single table type that is optimized to handle both transactional and analytical workloads. This eliminates the bottlenecks traditional monolithic databases face.
SingleStore addresses the enterprise issues by designing for scale from the ground. It makes use of distributed shared-nothing architecture with aggregators and leaf nodes. Data is automatically sharded across leaves by a user-defined key or hashing strategy, and each shard (partition) has a primary and replica copy on different nodes. The aggregator nodes act like smart coordinators: a query is received by a master aggregator, which then fans out sub-queries to the relevant leaf partitions in parallel.
Therefore, with its architectural benefits, it automatically supports horizontal scaling. It automatically rebalances partitions to carry the workloads.
Let us understand through a few real time enterprise examples where SingleStore proves to be more efficient than PostgreSQL.
Universal Storage: The in-memory row store and disk column store supports both OLTP and OLAP operations in a single database engine. This helps where data is ingested for real time transactions while updates are mirrored in column stores for analytics. This architecture helps in avoiding the Postgres workaround of offloading reads or analytics to separate systems. In practical terms, queries like aggregations or vector similarity searches run orders of magnitude faster on columnar data than on a Postgres rowstore.Due to SingleStore’s support for both OLTP and OLAP operations in a single machine, it reduces the latency caused by additional system adds and synchronization headaches. This allows the enterprise to make use of complex large analytical operations with ease and low latency.
Lock-free Concurrency: Generally, in PostgreSQL, each client connection is a separate process, and hence concurrent transactions often fail to process. On the contrary, SingeStore uses in-memory, lock-free data structures; its rowstore indexes are implemented as lock-free skip lists and hash tables. This helps in concurrent inserts, reads and updates without waiting for locks. For example, a SELECT in SingleStore will not block an UPDATE; each sees a consistent view via MVCC versions. To learn more on how the locks work in SingleStore, please follow SingleStore’s official documentation on How does SingleStore’s in-memory lock-free storage engine work?.
High availability & Auto Scaling capability: SingleStore’s replication mechanism is transparent. It has primary and more replicas on different nodes. This produces results without lag as we have often seen in PostgreSQL scenarios. Moreover, SingleStore Helios is a managed cloud service which offers seamless autoscaling capability. It creates a cluster and automatically intelligently adds CPU/RAM and other resources during the traffic spikes and shrinks back down when load normalizes. This dynamic scaling works at the compute layer (leaves/aggregators) while storage is decoupled.
MVCC, Storage and Durability: Both Postgres and SingleStore use MVCC, but the effects differ. In Postgres, MVCC means old row versions pile up on disk until a VACUUM sweeps them, causing bloat. In SingleStore, MVCC is done in-memory with skip lists and a background cleaner; there’s no heavy VACUUM step and no frozen bloat. Also, the storage engine differs too, PostgreSQL’s row oriented pages do not work well for high analytical scans where SingleStore’s Universal Storage project and compress for faster queries.
Postgress follows the concept of Write Head logging, where it might have to replay WAL entries after a crash on a single node. On the contrary. SingleStore writes the log immediately. This results in an up to date replica set and avoids the lag and inconsistency in data.
Through the above examples, we’ve seen how SingleStore’s distributed architecture and hybrid storage model eliminate the enterprise bottlenecks often observed with PostgreSQL. By enabling smooth traffic flow, reliable high-availability replication, and adaptive scaling, SingleStore provides durability, scalability, and consistently high performance exactly where PostgreSQL begins to struggle.
In the next sections, we’ll explore practical techniques for migrating data to SingleStore showing how the process can be both efficient and seamless, without sacrificing data integrity.
Migrating from PostgreSQL to SingleStore
Let's divide the steps to migrate in four different steps.
Migration Strategy
The first step when you wish to move the data from PostgreSQL to SingleStore is deciding on an approach based on your downtime tolerance and team capacity. This could be in three different ways:
Bulk Migration: This is suitable for historical data where a long downtime is acceptable.
Hybrid Migration: Use this for bulk data and follow with stream data for incremental changes. This migration accepts minimal downtime and lower risk.
Dual Migration: In this type, the application writes to both PostgreSQL and SingleStore for a transition period. This works where data loss is not acceptable but comes with a cost of complexity.
The next step is to assess and plan the postgres Schema, stored procedures and heavy production queries. This helps you manage the data on SingleStore better and more efficiently.
It is always recommended to save the most critical queries used against Postgres which could be used to validate against SingleStore deployment.
Schema Translation
Once the strategy is all set to be followed, the next step is where you would need to translate the PostgreSQL to SingleStore friendly DDL. SingleStore is MySQL-wire-compatible, so BIGSERIAL becomes BIGINT AUTO_INCREMENT, jsonb becomes JSON, and arrays should be converted to JSON or normalized child tables.
For example:
1-- Postgres DDL2CREATE TABLE clicks (3 click_id BIGSERIAL PRIMARY KEY,4 user_id INTEGER,5 page_id INTEGER,6 ts TIMESTAMPTZ7);8 9-- SingleStore DDL with shard and sort keys10CREATE TABLE clicks (11 click_id BIGINT AUTO_INCREMENT,12 user_id INT,13 page_id INT,14 ts TIMESTAMP,15 PRIMARY KEY (click_id, user_id),16 SHARD KEY (user_id),17 SORT KEY (ts, user_id)18);
Migrating Data
Once the strategy and tables are fixed, the first step would be to move the historical data. A common pattern is to export Postgres tables into CSV or Parquet files and upload them to cloud object storage. From there, SingleStore pipelines can bulk-ingest the data in parallel across all nodes.
For example: create a table to load the data by creating a pipeline as given in the example below:
1CREATE TABLE orders (2 order_id BIGINT AUTO_INCREMENT,3 customer_id INT,4 amount DECIMAL(10,2),5 created_at TIMESTAMP,6 PRIMARY KEY (order_id, customer_id),7 SHARD KEY (customer_id),8 SORT KEY (created_at)9);10 11CREATE PIPELINE load_orders12AS LOAD DATA S3 's3://my-mig-bucket/orders/*.csv'13CREDENTIALS '{"aws_access_key_id":"AKIA...","aws_secret_access_key":"..."}'14CONFIG '{"region":"us-west-2"}'15INTO TABLE orders16FIELDS TERMINATED BY ',';
Once the pipeline is loaded, the next step is to keep Postgres and SingleStore in sync while users continue writing data. This is usually done with Change Data Capture (CDC).
To do so:
1CREATE PIPELINE cdc_orders2AS LOAD DATA KAFKA 'kafka-bootstrap:9092'3TOPIC 'dbserver1.public.orders'4INTO TABLE orders5FORMAT JSON 'debezium_envelope';6 7START PIPELINE cdc_orders;
With continuous migration happening, the data between PostgreSQL and SingleStore will be synced in real time.
The next step is connecting the application with the SingleStore connection string. For example, if you have a Java application, you can add the below to the application code as:
1String connection = String.format("jdbc:mariadb://%s:%s/%s", HOSTNAME, PORT, DATABASE);2Connection conn = DriverManager.getConnection(connection, USER, PASSWORD);
For more information, you can refer to the documentation on Connecting to Your Application Development Tools.
If you have your application deployed in MongoDB, using SingleStore’s Kai is the most efficient way to connect your MongoDB based application with SingleStore without the need to need any of the above.
Post Migration checklist
Once the data has been moved into SingleStore, below are a few important points to be kept in mind.
Ensure schema compatibility by mapping Postgres-specific data types and features to SingleStore equivalents.
Define shard and sort keys carefully to balance workloads and avoid data skew.
Plan the data migration strategy using bulk loads for history and pipelines/CDC for ongoing sync.
Update applications and queries to work with SingleStore’s MySQL wire protocol.
Validate migrated data and keep a rollback plan until SingleStore is stable in production.
Conclusion
Enterprise application in today’s world cannot afford inefficiency. PostgreSQL is no doubt first choice for an early stage application, but as businesses grow and customer expectations demand real-time, always-on experiences, its limits quickly appear, whether that’s downtime during peak traffic, lagging analytics, or spiraling infrastructure costs.
SingleStore helps with the application bottlenecks. It unifies transactions and analytics in one engine, eliminates replication delays, and scales elastically with your business, so you can focus on delivering innovation instead of firefighting database performance issues. From powering real-time dashboards to supporting millions of global users with zero lag, SingleStore enables you to meet the speed, scale, and reliability modern enterprises demand.
To learn more about SingleStore, Try your first free trial of SingleStore Helios and explore the possibilities. Read about our customer success stories about how leading enterprises scale with SingleStore and official documentations to learn more.
