Migrating from Elasticsearch

This page is for developers who are evaluating or actively migrating from Elasticsearch (or OpenSearch) to ParadeDB. ParadeDB delivers Elastic-quality full-text search as a Postgres extension, so your data, queries, and infrastructure all live inside Postgres — no ETL pipelines, no separate cluster to manage.

Key Differences

	Elasticsearch	ParadeDB
Query language	JSON DSL	Standard SQL with search operators
Data model	Denormalized documents	Normalized relational tables with JOINs
Transactions	Per-document atomicity, eventual consistency	Full ACID transactions
Index storage	Separate cluster	Inside Postgres (same database)
Schema changes	Dynamic mapping or reindex	Defined at index creation; `REINDEX` to change
Updates/deletes	Expensive (reindex internally)	Native Postgres operations

Migration Tips

Start with your most common queries. Map your highest-traffic Elasticsearch queries using the full-text search reference.
Use SQL JOINs instead of denormalization. Elasticsearch requires denormalized documents, but ParadeDB supports full SQL JOINs. You can normalize your schema and simplify your data model.
Continue to use Postgres tooling. Backups, replication, monitoring, and CI/CD integrate with standard Postgres tools you already use.

Feature Comparison

Query Capabilities

Feature	Elasticsearch	ParadeDB	Notes
Full-text search (BM25)	✅	✅	Match and phrase operators
Fuzzy matching	✅	✅	Max edit distance of 2 via `pdb.fuzzy()`
Phrase matching	✅	✅	`###` operator
Phrase prefix	✅	✅	`pdb.phrase_prefix()`
Regular expressions	✅	✅	`pdb.regex()`
Wildcard queries	✅	✅	Via regex
Boolean queries	✅	✅	Via SQL `AND`/`OR`/`NOT` or `paradedb.boolean`
Proximity search	✅	✅	`##` operator
More Like This	✅	✅	`paradedb.more_like_this`
Nested queries	✅	✅	Via SQL `JOIN`s
Parent-child queries	✅	✅	Via SQL `JOIN`s
Geo queries	✅	❌	Use PostGIS
Percolator (reverse search)	✅	❌
Script-based scoring	✅	❌
Suggesters (autocomplete)	✅	✅	Via search_tokenizer (index with ngram, search with unicode) or fuzzy prefix

Text Analysis

Feature	Elasticsearch	ParadeDB	Notes
Custom tokenizers	✅	✅	12+ built-in tokenizers
Token filters	✅	✅	7 filters: lowercase, stemmer, stopwords, ascii_folding, alpha_num_only, trim, token_length
Character filters	✅	❌
Synonyms	✅	⚠️	Coming soon
Different search-time analyzer	✅	✅	Via search_tokenizer or multiple tokenizers per field
Multi-language support	✅	✅	Chinese (Jieba), Japanese/Korean (Lindera), ICU
Stemming	✅	✅	19 languages
Stopwords	✅	✅	29 languages
N-gram tokenization	✅	✅	Configurable min/max gram size

Aggregations

Feature	Elasticsearch	ParadeDB	Notes
Bucket aggregations	✅	✅	terms, histogram, date_histogram, range, filters
Metric aggregations	✅	✅	avg, sum, min/max, count, stats, percentiles, cardinality, top_hits
Pipeline aggregations	✅	✅	Use SQL window functions (`SUM() OVER()`, `LAG()`, etc.)
Nested aggregations	✅	✅	Use `pdb.agg()` with SQL `GROUP BY`
ES-compatible JSON syntax	—	✅	`pdb.agg()` accepts ES JSON directly
SQL GROUP BY	Limited	✅	Full SQL aggregation support

Since pdb.agg() accepts Elasticsearch-compatible JSON, many of your existing aggregation queries can be migrated with minimal changes. See the aggregates documentation.

Scoring and Relevance

Feature	Elasticsearch	ParadeDB	Notes
BM25 scoring	✅	✅	`pdb.score()`
Custom boost	✅	✅	`pdb.boost()` type cast
Constant score	✅	✅	`pdb.const()`
Disjunction max	✅	✅	`paradedb.disjunction_max()`
Function score	✅	❌	Use boost / const as partial alternatives
Script scoring	✅	❌
Decay functions	✅	❌
Field value factor	✅	❌

Highlighting

Feature	Elasticsearch	ParadeDB	Notes
Snippet highlighting	✅	✅	`pdb.snippet()`
Multiple snippets	✅	✅	`pdb.snippets()`
Custom tags	✅	✅	`start_tag`, `end_tag` parameters
Byte offsets	❌	✅	`pdb.snippet_positions()`
Fuzzy highlighting	✅	❌

Index Management

Feature	Elasticsearch	ParadeDB	Notes
Create index	✅	✅	`CREATE INDEX ... USING bm25`
Drop index	✅	✅	`DROP INDEX`
Reindex	✅	✅	`REINDEX`
Index aliases	✅	✅	Via Postgres views
Index templates	✅	❌
Dynamic mapping	✅	❌	Schema defined at index creation; requires `REINDEX` to change
Multi-field index	✅	✅	All columns included in one index per table

Data Operations

Feature	Elasticsearch	ParadeDB	Notes
ACID transactions	❌	✅	Full Postgres ACID compliance
Real-time indexing	⚠️	✅	ES is near-real-time (requires refresh); ParadeDB provides immediate read-after-write
JOINs	❌	✅	Full SQL JOIN support
UPDATE / DELETE	⚠️	✅	ES internally reindexes; Postgres handles natively
Bulk insert	✅	✅	`COPY` or batch `INSERT`
SQL queries	❌	✅	Full SQL including subqueries, CTEs, window functions

Deployment and Operations

Feature	Elasticsearch	ParadeDB	Notes
Horizontal sharding	✅	⚠️	Via Citus for distributed workloads
Read replicas	✅	✅	Postgres streaming replication
Kubernetes	✅	✅	CNPG / Helm charts
Docker	✅	✅	Official Docker image
Logical replication ingest	❌	✅	Sync from existing Postgres
Cross-cluster search	✅	❌
Snapshot / restore	✅	✅	Via Postgres backup tools (pg_dump, WAL archiving)
Monitoring	✅	✅	pg_stat, pganalyze, standard Postgres tools

Pagination

Feature	Elasticsearch	ParadeDB	Notes
`from` / `size`	✅	✅	SQL `LIMIT` / `OFFSET`
`scroll` API	✅	❌	Use SQL cursors (`DECLARE` / `FETCH`) instead
`search_after`	✅	❌	Use keyset pagination (`WHERE id > last_id ORDER BY id`) instead
Top K optimization	✅	✅	`paradedb.limit_fetch_multiplier`

​Key Differences

​Migration Tips

​Feature Comparison

​Query Capabilities

​Text Analysis

​Aggregations

​Scoring and Relevance

​Highlighting

​Index Management

​Data Operations

​Deployment and Operations

​Pagination

Key Differences

Migration Tips

Feature Comparison

Query Capabilities

Text Analysis

Aggregations

Scoring and Relevance

Highlighting

Index Management

Data Operations

Deployment and Operations

Pagination