Red9 Database Popularity Index

The Most Popular Databases in 2026: A Live Popularity Ranking

Picking a database is a ten-year bet: it decides who you can hire, what your tools support, and how easy help is to find at 3 a.m. This index tracks where real demand is heading for 64 systems, re-ranked every month, so you see the trend before you commit.

64Systems tracked
Jul 2026Data month
Sep 2Next update
Current leader
117.9
PostgreSQL
index score · Jul 2026
Closest race
3.8
PostgreSQL over Databricks
points between #1 and #2
Developer activity
115.6
Redis
developer-side sub-score
Market demand
120.1
PostgreSQL
market-side sub-score

Full Database Popularity Ranking

Rank by i
# Database Score i 12-mo interest i Trend i
1PostgreSQLRelational117.9▲6%
2DatabricksAnalytical114.1▲8%
3RedisDoc/KV114.1▼19%
4SnowflakeAnalytical112.9▼2%
5MySQLRelational112.7▼16%
6Microsoft SQL ServerRelational112.6▼34%
7MongoDBDoc/KV109.9▼26%
8Oracle DatabaseRelational109.3▼14%
9ElasticsearchSearch109.1▼3%
10Google BigQueryAnalytical108.3▼9%
11SQLiteRelational107.3▲21%
12PrometheusTime-series107.0▼25%
13ClickHouseAnalytical105.2▲21%
14SupabaseprovisionalNewSQL/Edge104.6·
15IBM Db2Heritage104.1▼24%
16DuckDBAnalytical103.8
17MariaDBRelational102.7▼4%
18Neo4jGraph/WC102.7▼4%
19Amazon DynamoDBDoc/KV102.6▼33%
20Firebase (Firestore & Realtime Database)Doc/KV102.6▼43%
21TeradataHeritage102.3▲19%
22Amazon RedshiftAnalytical101.3▼47%
23WeaviateVector101.3·
24QdrantVector101.2·
25SplunkSearch101.0▼37%
26PineconeVector100.1·
27Azure Cosmos DBDoc/KV99.7▼36%
28OpenSearchSearch99.3▼6%
29SAP HANAHeritage99.3▼18%
30TrinoAnalytical99.3▼45%
31ValkeyprovisionalDoc/KV99.0▼44%
32Apache CassandraGraph/WC98.3▼39%
33InfluxDBTime-series97.9▼25%
34CockroachDBNewSQL/Edge97.8▼4%
35MilvusVector97.8▼34%
36AlgoliaSearch97.4▼15%
37MemcachedDoc/KV97.3▼22%
38TimescaleDBTime-series97.2▼32%
39VerticaHeritage96.7▼18%
40ChromaprovisionalVector96.5·
41Apache SolrSearch96.3▼37%
42Azure SQL DatabaseRelational95.9▼23%
43VictoriaMetricsTime-series94.9·
44Google Cloud SpannerNewSQL/Edge94.6▼20%
45SAP ASE (Sybase)Heritage93.8▼26%
46kdb+Time-series93.7▼17%
47LanceDBprovisionalVector93.7·
48IBM InformixHeritage93.6
49Apache HiveAnalytical93.4▼36%
50Amazon AuroraRelational93.0▼43%
51etcdDoc/KV92.9▼27%
52ArangoDBGraph/WC92.1▼32%
53TiDBNewSQL/Edge92.1▼31%
54ScyllaDBGraph/WC92.0▼31%
55Apache CouchDBDoc/KV91.8▼27%
56CouchbaseDoc/KV91.8▼52%
57Apache HBaseGraph/WC91.5▼43%
58FirebirdHeritage91.1▼10%
59YugabyteDBNewSQL/Edge90.9▼15%
60QuestDBTime-series90.1·
61NeonprovisionalNewSQL/Edge89.8·
62Turso (libSQL)provisionalNewSQL/Edge88.5·
63SurrealDBprovisionalNewSQL/Edge86.9·
64H2 DatabaseHeritage86.6▼12%
Showing all 64 systems · ranked by overall score

Database Popularity Trends: 12-Month View i

These lines show public interest until the index has enough score history of its own. Compare up to 8 systems: click a name to hide its line, hit × to drop it.

Categories

Most Popular Databases by Category

One leaderboard flattens nine different jobs. A vector store and a mainframe-era relational engine are never competing for the same slot, so the category pills above the table split the ranking into the races that actually happen. Here is what each one covers.

What Each Category Ranks

  • Relational. The classic SQL engines that still run most transactional systems. Popularity moves slowly here and rewards decades of tooling, drivers, and deep hiring pools.
  • Analytical. Warehouses and lakehouses built for scans rather than single-row lookups. The fastest-moving enterprise category in the index.
  • Vector. Embedding stores for search and retrieval. Young, crowded, and the one category where a single year of momentum can rewrite the order.
  • Document and key-value. Schema-light stores and caches. Developer signals dominate, because adoption starts in a side project long before it reaches procurement.
  • Time-series. Engines tuned for metrics and event streams. Observability budgets drive this group more than application teams do.
  • Graph and wide-column. Specialist engines chosen for one shape of query. Small categories, so a single strong release shows up clearly.
  • Search. Inverted-index engines that sit beside a primary database. Ranked separately because they are rarely the system of record.
  • NewSQL and edge. Distributed SQL and embedded engines. Heavy developer interest, thinner hiring demand, and the widest gap between our two sub-scores.
  • Heritage. Systems past their commercial peak that still run critical workloads. Low momentum, real installed base, and the hardest group to staff.
Reading the Index

Database Popularity vs Market Share

The two terms get used interchangeably and they measure opposite ends of the same story. Market share counts what is already installed. That is a rear-view number, and in databases the rear view is long: a system can lose every new project for five years and still hold its share, because nobody rips out a database that works. Popularity counts what teams are hiring for, reading about, downloading, and arguing over in 2026. It moves first.

Which Number Answers Which Question

  • How easy is this to support today? Market share. A large installed base means consultants, forum answers, and a hiring pool that already exists.
  • How easy will it be to support in ten years? Popularity, and specifically its direction. The 12-month trend above is the part of this page worth acting on.
  • Is this system still winning new projects? Popularity. Share can stay flat for years after a system stops being chosen for anything new.
  • Should we migrate off? Neither on its own. A four-quarter slide is a reason to look closely, not a reason to move.
  • Which should we build on? Neither. Popularity describes the market around a database. Fit depends on your workload, your team, and your budget.
  • Why do the two disagree so often? Because hiring, reading, and downloading all happen before a system is installed, and share only counts what already is.
FAQ

Frequently Asked Questions

Which database is the most popular right now?

PostgreSQL leads the Jul 2026 index at 117.9, ahead of Databricks (114.1) and Redis (114.1).

What feeds the score?

Six families of public demand signals: hiring activity, reader interest, infrastructure activity, developer adoption, community questions, and technical discussion. We publish the families and the full monthly results. The exact sources, weights, and formulas stay private.

Why isn't the #1 database scored 100?

100 marks the average database, and the leader sits above the average. If 100 were pinned to the leader instead, every other score would drop whenever #1 surged, even with nothing else changing. Anchoring to the average avoids that.

How often does this update?

Monthly, in the first days of the month. Every chart carries the data month it was computed from.

Is Databricks really a database?

Databricks is a lakehouse platform with a database engine at its core, and database rankings have long listed it alongside warehouses like Snowflake and BigQuery. Each row carries a category tag, so filter by Relational if you want the classic engines only.

Methodology

How We Rank Database Popularity

Every month we collect several dozen measurements per database from public sources and roll them into six weighted signal families. We keep the exact sources, weights, and formulas private. That is what makes the index hard to game. Two sub-scores read the same data from different angles: developer activity covers code, adoption, and discussion; market demand covers hiring and mainstream interest.

What This Ranking Does Not Measure

  • Installed base. Nothing public counts production servers. This index measures attention and demand, which lead adoption.
  • Question volume. Community Q&A partly rewards the databases people struggle with, so it carries one of the smallest weights in the blend.
  • Job ads lag reality by months, and enterprise systems are louder there than open-source ones. The two sub-scores show each world separately.
  • Ecosystems blur at the edges. Wire-compatible services ride their parent database’s drivers, and no ranking can split that traffic; ours re-balances the compatibles across the signals they do own. Download registries also skew open-source, so the hiring and readership signals carry the enterprise side that registries miss.
  • Adjacent ranks are noise. Treat #7 vs #8 as a tie. Treat #7 vs #15 as a real gap.

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