Elasticsearch vs Parseable for Log Analytics: 2026 Architecture Comparison

Updated: February 2026 | Reading time: 18 min

Introduction

Elasticsearch has been the default backend for log analytics for over a decade. It powers the ELK stack, underpins countless Kibana dashboards, and has become so synonymous with log search that many engineers cannot imagine an alternative. But the architecture decisions that made Elasticsearch dominant in 2014 are the same decisions that make it painful to operate in 2026.

Running Elasticsearch for logs at scale means managing JVM heap sizes, tuning shard counts, fighting split-brain scenarios, configuring index lifecycle management policies, and paying for expensive SSD storage to keep Lucene segments performant. It means dedicating entire engineering teams to cluster operations instead of building product features. And it means accepting that your log storage backend was designed as a general-purpose search engine, not as a purpose-built observability platform.

Parseable takes a fundamentally different approach. Built in Rust as a single binary, Parseable writes telemetry data directly to S3-compatible object storage in Apache Parquet format. There are no JVM processes to tune, no shards to manage, no cluster coordination to debug. And unlike Elasticsearch, which handles logs only, Parseable is a unified MELT observability platform covering logs, metrics, events, and traces through a single deployment — available as open source or as Parseable Cloud for zero-ops managed observability.

This guide provides a deep, architecture-level comparison of Elasticsearch and Parseable for log analytics. If you are a platform engineer evaluating an Elasticsearch replacement for logging, or an SRE tired of babysitting Elasticsearch clusters at 3 AM, this is the comparison you need.

Architecture Deep Dive: Lucene Indexes vs S3 Parquet

The fundamental architectural difference between Elasticsearch and Parseable is how they store and retrieve data. Everything else, operational complexity, cost, scalability, and query performance, flows from this core design decision.

Elasticsearch: Inverted Indexes on Local Disk

Elasticsearch is built on top of Apache Lucene, a Java-based full-text search library. When a log line arrives at an Elasticsearch node, the following sequence occurs:

1. Ingestion buffer: The document enters an in-memory buffer on the coordinating node.
2. Routing: A hash of the document's _id (or routing key) determines which shard receives the document.
3. Translog write: The document is written to a transaction log (translog) on local disk for durability.
4. In-memory index buffer: The document enters a per-shard in-memory buffer.
5. Refresh: Every 1 second (by default), the in-memory buffer is flushed to a new Lucene segment on disk. The segment contains an inverted index mapping every term to the documents containing it.
6. Merge: Background merge operations combine small segments into larger ones, reclaiming space from deleted documents and optimizing query performance.
7. Flush: Periodically, the translog is truncated and a full Lucene commit is written to disk.

This design was built for full-text search of relatively small documents, think web pages and product catalogs, where you need sub-second keyword lookups. For log analytics, it creates several problems:

• Storage amplification: Inverted indexes store every unique term with positional data and document frequency metadata. For high-cardinality log fields like request IDs, trace IDs, and IP addresses, this creates significant storage overhead. In practice, Elasticsearch uses 1.5x to 3x the raw data size in storage.
• SSD dependency: Lucene segments require random-access reads during queries. Segment merges are I/O-intensive operations that thrash spinning disks. Production Elasticsearch clusters require SSDs, period.
• JVM heap pressure: Lucene's segment metadata, field data caches, and query caches all consume JVM heap. The recommended heap size for a production data node is 30-31 GB (below the compressed oops threshold), which means each data node needs at least 64 GB of RAM to leave room for the OS page cache.
• Write amplification: Segment merges rewrite data multiple times. A single document may be rewritten 5-10 times during its lifecycle as segments merge from tier to tier.

Parseable: Columnar Parquet on Object Storage

Parseable's storage architecture is fundamentally different. When telemetry data (logs, metrics, events, or traces) arrives at a Parseable instance:

1. In-memory staging: Data is buffered in memory using efficient Rust data structures with minimal overhead. Parseable's Rust runtime has no garbage collector, so there are no GC pauses or heap sizing concerns.
2. Columnar conversion: Buffered data is converted to Apache Parquet columnar format. Parquet stores data column-by-column rather than row-by-row, which is ideal for analytical queries that typically access a subset of fields across many rows.
3. Compression: Parquet applies column-level compression using algorithms like Snappy, Zstandard, or LZ4. Because each column contains homogeneous data types, compression ratios are excellent, typically 5x to 15x depending on the data.
4. Object storage write: Compressed Parquet files are written directly to S3, MinIO, GCS, Azure Blob Storage, or any S3-compatible backend. This is a sequential write operation, not a random I/O pattern.

The implications of this architecture are profound:

• No storage amplification: Parquet with compression typically achieves 70-90% size reduction compared to raw JSON logs. Your stored data is smaller than the original input, not larger.
• No SSD requirement: Object storage is the backend. S3 provides 11 nines of durability, automatic replication across availability zones, and costs $0.023/GB/month for standard storage. There are no disks to manage, replace, or monitor.
• Minimal memory footprint: Parseable's Rust runtime operates with less than 50 MB of base memory. There is no JVM, no garbage collector, no heap tuning. A Parseable instance serving production workloads uses a fraction of the memory that a single Elasticsearch data node requires.
• No write amplification: Data is written once to object storage. There are no background merge operations rewriting your data repeatedly.

What This Means in Practice

Consider an organization ingesting 500 GB of raw logs per day with 30-day retention.

Elasticsearch storage requirement:

• Raw data: 500 GB/day x 30 days = 15 TB
• With index overhead (2x average): 30 TB of SSD storage
• With one replica (standard for production): 60 TB of SSD storage
• At $0.10-0.30/GB for provisioned SSD (cloud EBS gp3/io2): $6,000 to $18,000/month in storage alone

Parseable storage requirement:

• Raw data: 500 GB/day x 30 days = 15 TB
• With Parquet compression (10x average): 1.5 TB on S3
• S3 standard storage at $0.023/GB: $34.50/month in storage
• With S3 cross-region replication for DR: $69/month

That is not a typo. The storage cost difference between Elasticsearch on SSD and Parseable on S3 is two to three orders of magnitude. Even accounting for S3 API call costs and data retrieval fees, Parseable's total storage cost remains under $200/month for this workload. Elasticsearch's storage cost exceeds $6,000/month at the low end.

Storage Model Comparison: The Economics

Storage architecture determines the long-term economics of your observability platform. Here is a detailed cost breakdown across different daily ingestion volumes, assuming 30-day retention.

These numbers account only for storage. When you add compute costs, the gap widens further. Elasticsearch requires large instances with significant memory for JVM heap and OS page cache. Parseable runs on minimal compute because the heavy lifting happens at query time using efficient Parquet scans, and at rest, the data sits on virtually free object storage.

For organizations that need longer retention periods, the economics become even more lopsided. Elasticsearch's Index Lifecycle Management (ILM) can move old indexes to cheaper "warm" or "cold" nodes, but those nodes still require provisioned compute and attached storage. With Parseable, extending retention from 30 days to 365 days simply means storing more Parquet files on S3. Moving data to S3 Glacier for archival costs $0.004/GB/month or less.

Operational Complexity: ES Cluster Management vs Single Binary

This is where the day-to-day pain lives. Running Elasticsearch in production is a full-time job. Running Parseable is not.

Elasticsearch Operational Burden

A production Elasticsearch deployment for log analytics at moderate scale (say 200 GB/day) typically involves:

Cluster topology:

• 3 dedicated master nodes (for quorum and split-brain prevention)
• 3-6 data nodes (for shard storage and query execution)
• 1-2 coordinating nodes (for query routing and aggregation)
• 1 Kibana instance
• Logstash or Beats for ingestion pipeline

That is 9-13 instances before you even account for redundancy, monitoring, or staging environments.

Ongoing operational tasks:

• Shard management: Each Elasticsearch index is divided into shards, and each shard is a Lucene instance. You must choose the right number of primary shards at index creation time (it cannot be changed later without reindexing). Too few shards limit write throughput. Too many shards create cluster state overhead, slow down queries, and increase heap usage on master nodes. The general guidance is to keep shards between 10-50 GB each, but this requires constant monitoring and adjustment as data patterns change.
• JVM heap tuning: Every data node runs a JVM that needs careful heap sizing. Set the heap too low and you get OutOfMemoryError crashes. Set it too high and you starve the OS page cache, destroying query performance because Lucene relies on the filesystem cache for segment reads. The sweet spot is roughly 50% of available RAM, capped at 30-31 GB to stay below the compressed oops threshold. Getting this wrong causes cascading failures.
• Index Lifecycle Management (ILM): Logs are time-series data, so you need automated policies to roll over indexes, move old indexes to cheaper storage tiers, and eventually delete expired data. ILM policies are notoriously finicky: they can stall on large shards, conflict with snapshot operations, and silently stop working if the cluster state becomes unhealthy.
• Cluster health monitoring: Elasticsearch has three cluster health states: green, yellow, and red. Yellow means some replica shards are unassigned. Red means some primary shards are unassigned, and you are losing data. Monitoring and reacting to cluster health changes is a constant operational concern. Unassigned shards can result from disk watermarks being breached, nodes leaving the cluster, or shard allocation settings being misconfigured.
• Rolling upgrades: Upgrading Elasticsearch requires a carefully orchestrated rolling restart process. You must disable shard allocation, stop a node, upgrade it, restart it, re-enable allocation, wait for the cluster to rebalance, and repeat for every node. A version upgrade across a 10-node cluster can take hours and carries real risk of data loss if something goes wrong.
• Mapping explosions: Elasticsearch dynamically creates field mappings based on the first document it sees for a given field name. If your logs contain fields with unpredictable names (common with JSON-structured logs from microservices), you can hit the index.mapping.total_fields.limit (default 1000). Exceeding this limit causes ingestion failures. Increasing the limit bloats cluster state and slows down queries.
• Circuit breaker management: Elasticsearch uses circuit breakers to prevent queries from consuming too much heap. When a circuit breaker trips, the query fails. This is common with high-cardinality aggregations, large time ranges, or complex bool queries against dense indexes. Tuning circuit breaker thresholds without destabilizing the cluster is an art form.

Parseable Operational Reality

Parseable's operational model is the opposite of Elasticsearch's:

Deployment: A single binary. No master nodes, no data nodes, no coordinating nodes, no Kibana, no Logstash. You run parseable with a configuration file pointing to your S3 bucket, and you have a fully operational observability platform.

Ongoing operational tasks:

• There are no shards to manage. Parseable does not use indexes or shards. Data is organized by log stream and time partition, written as Parquet files to object storage. There is no shard sizing, no reindexing, no shard allocation debugging.
• There is no JVM to tune. Parseable is written in Rust and compiles to a native binary. Memory usage is predictable, deterministic, and minimal. There are no garbage collection pauses, no heap sizing decisions, no compressed oops thresholds.
• There is no ILM. Retention is configured as a simple time-based policy per log stream. S3 lifecycle rules handle the rest. No stalled policies, no conflicting operations, no silent failures.
• There are no mapping explosions. Parseable handles schema evolution natively. New fields in your logs are automatically incorporated without hitting field limits or requiring mapping updates.
• Upgrades are a binary swap. Stop the old binary, start the new one. There is no cluster rebalancing, no shard allocation dance, no rolling restart orchestration.

The operational complexity difference is not incremental. It is categorical. Elasticsearch demands a dedicated team. Parseable can be operated as a side responsibility by any engineer on the platform team.

Query Language: KQL/Lucene vs SQL

The query language determines how quickly your team can investigate incidents and extract insights from telemetry data. If you are also comparing visualization layers, see our Kibana vs Parseable Console guide for a head-to-head breakdown of dashboards, live tail, and alerting.

Elasticsearch: KQL and Lucene Query Syntax

Elasticsearch exposes two primary query interfaces:

Kibana Query Language (KQL): Used in Kibana's search bar, KQL provides a simplified syntax for filtering:

status: 500 and service: "api-gateway" and not path: "/health"

KQL is readable but limited. It supports field-level filtering with basic boolean logic but cannot perform aggregations, transformations, or joins.

Lucene Query Syntax: The lower-level query language supports range queries, wildcards, regex, and fuzzy matching:

status:[500 TO 599] AND service:api-gateway AND path:/\/api\/v[0-9]+\/.*/

Elasticsearch Query DSL: For complex queries, you drop into JSON-based Query DSL, which is verbose and error-prone:

{
  "query": {
    "bool": {
      "must": [
        { "range": { "status": { "gte": 500, "lte": 599 } } },
        { "term": { "service": "api-gateway" } }
      ],
      "must_not": [
        { "term": { "path": "/health" } }
      ]
    }
  },
  "aggs": {
    "errors_per_minute": {
      "date_histogram": {
        "field": "@timestamp",
        "calendar_interval": "minute"
      }
    }
  }
}

This 20-line JSON does what a single SQL statement can express. The Query DSL is powerful, but it requires specialized knowledge that most engineers do not have. Teams typically rely on a small number of "Elasticsearch experts" to write complex queries, creating a bottleneck during incidents.

Parseable: Standard SQL

Parseable uses standard SQL as its query language. The same query from the Elasticsearch example above:

SELECT
  date_trunc('minute', p_timestamp) AS minute,
  COUNT(*) AS error_count
FROM
  api_logs
WHERE
  status BETWEEN 500 AND 599
  AND service = 'api-gateway'
  AND path != '/health'
  AND p_timestamp > NOW() - INTERVAL '1 hour'
GROUP BY
  minute
ORDER BY
  minute DESC;

This is a language that virtually every engineer already knows. There is no proprietary syntax to learn, no JSON DSL to memorize, and no translation layer between what you want to ask and how you express it. Junior engineers, backend developers, data analysts, and SREs can all write Parseable queries on day one.

SQL also means you can use existing SQL tools, libraries, and integrations. Anything that speaks SQL can query Parseable data, from business intelligence tools to custom scripts to AI-powered query generators.

Quick Comparison Table

Scaling: Elasticsearch Clusters vs Parseable

Elasticsearch Scaling Challenges

Scaling Elasticsearch is a multi-dimensional problem that requires simultaneous consideration of:

• Write throughput: Adding data nodes increases write capacity, but only if you have enough primary shards to distribute writes. If your index has 5 primary shards, you cannot effectively distribute writes across more than 5 data nodes for that index.
• Query performance: Queries fan out to all shards. More shards mean more parallel execution but also more coordination overhead. The coordinating node must merge results from every shard, which becomes a bottleneck for aggregation-heavy queries.
• Cluster state: Every shard adds metadata to the cluster state, which all nodes must maintain in memory. Clusters with millions of shards (common in time-series use cases with daily indexes) experience slow cluster state updates, master node instability, and delayed shard allocation.
• Storage scaling: You cannot simply add storage to an existing node. If a data node's disk fills up, you must either add a new node (and rebalance shards), increase disk size (if your cloud provider supports online resize), or delete data.
• Cross-cluster search: When a single cluster cannot handle the load, Elasticsearch supports cross-cluster search (CCS). But CCS introduces network latency, increases failure modes, and requires careful management of cluster trust relationships and network connectivity.

In practice, Elasticsearch scaling follows a staircase pattern: you periodically hit a ceiling, engage in a multi-day project to add capacity and rebalance, hit the next ceiling, and repeat.

Parseable Scaling Model

Parseable's scaling model leverages the inherent scalability of object storage:

• Write throughput: Parseable instances can scale horizontally. Multiple ingest nodes write to the same S3 bucket using partitioned prefixes. There is no shard allocation or rebalancing required. Scaling write throughput is as simple as adding another Parseable instance behind a load balancer.
• Query performance: Parquet's columnar format enables efficient predicate pushdown and column pruning. Queries read only the columns they need, and time-based partitioning means queries scan only the relevant time range. As data volumes grow, query performance degrades linearly with the amount of data scanned, not exponentially as with Lucene segment merges.
• Storage scaling: S3 has no capacity limits. You do not provision storage, manage disk utilization, or plan capacity. Your storage scales automatically and infinitely as data accumulates. Costs are purely pay-per-use.
• Retention scaling: Extending retention from 30 days to 1 year is a configuration change, not an infrastructure project. The data is already on S3. You are just changing a lifecycle policy.

The fundamental difference is that Parseable separates compute from storage. You scale compute (Parseable instances) independently from storage (S3). Elasticsearch couples compute and storage on every data node, which means scaling one dimension forces you to scale both.

Why Parseable Is the Better Elasticsearch Alternative for Logs

For teams currently running Elasticsearch for log analytics, Parseable is not just an alternative. It is a generational improvement across every dimension that matters in production.

Eliminate Cluster Operations Entirely

The single most impactful benefit of migrating from Elasticsearch to Parseable is eliminating cluster operations. Every hour your platform team spends debugging unassigned shards, tuning JVM heap, orchestrating rolling upgrades, or fighting ILM stalls is an hour they are not spending on product features, developer experience, or infrastructure improvements that move the business forward.

Parseable reduces your observability infrastructure to a single binary and an S3 bucket. The binary handles ingestion, storage, querying, and visualization. The S3 bucket provides durable, replicated, infinitely scalable storage at commodity pricing. There is nothing else to operate. No master nodes to keep in quorum. No circuit breakers to tune. No mapping explosions to debug. No shard allocation to monitor.

For a typical organization running a 10-node Elasticsearch cluster, this translates to eliminating the equivalent of 0.5 to 1.0 full-time engineers worth of cluster management overhead. At a fully loaded cost of $200,000 per engineer, that is $100,000 to $200,000 per year in operational savings before you even account for storage cost reductions.

Unified MELT Observability, Not Just Logs

Elasticsearch is a log storage backend. It does one thing (store and search text data) and requires a sprawling ecosystem of other tools to provide anything resembling complete observability. Kibana for visualization. Beats for collection. Logstash for processing. APM Server for traces. Metricbeat for metrics. Each component adds operational complexity, failure modes, and integration maintenance.

Parseable is a unified observability platform that handles logs, metrics, events, and traces (MELT) through a single deployment. It provides a native OTLP (OpenTelemetry Protocol) endpoint, which means you can point any OpenTelemetry Collector or SDK directly at Parseable and send all telemetry types to one platform. No APM Server. No Metricbeat. No separate backends for different signal types.

This unification is not just about reducing tool count. It enables cross-signal correlation that is impossible when your logs, metrics, and traces live in separate systems. When an alert fires on a metric, you can immediately pivot to the corresponding logs and traces without switching tools, translating queries between different languages, or mentally correlating timestamps across separate UIs.

Storage Costs That Scale With Reality

Elasticsearch's storage model was designed when SSDs were the fastest storage available and object storage was slow and expensive. In 2026, S3 provides throughput that is more than sufficient for analytical workloads, at prices that make SSD storage look absurd for time-series data.

Parseable's S3-native architecture means your storage cost tracks linearly with the amount of data you retain, at object storage pricing. There is no index overhead, no replica multiplication, and no SSD premium. For a team ingesting 500 GB/day with 90-day retention, the difference is stark:

• Elasticsearch: 500 GB x 90 days x 2 (index overhead) x 2 (replica) x $0.15/GB = $27,000/month
• Parseable (self-hosted): 500 GB x 90 days / 10 (compression) x $0.023/GB = $103.50/month

Parseable Cloud Pricing: For teams that prefer managed infrastructure, Parseable Cloud starts at $0.37/GB ingested with 30-day retention, minimum $29/month. No Elasticsearch clusters to manage, no SSD storage to provision.

The 260x self-hosted cost difference is not an exaggeration or a cherry-picked scenario. It is the straightforward result of storing compressed columnar data on object storage instead of replicated inverted indexes on SSDs. Even if you add generous estimates for S3 API costs, data transfer, and compute, Parseable's total cost of ownership is less than 5% of Elasticsearch's for the same workload.

Rust Performance Without JVM Overhead

Elasticsearch runs on the JVM. That means garbage collection pauses, heap sizing decisions, class loading overhead, and a base memory footprint measured in gigabytes. A single Elasticsearch data node needs 30+ GB of JVM heap plus an equivalent amount of OS memory for the filesystem page cache. That is 64 GB of RAM per node before your data even arrives.

Parseable is written in Rust, which compiles to a native binary with no runtime overhead. There is no garbage collector. Memory allocation is deterministic and predictable. The base memory footprint is under 50 MB. A single Parseable instance running a production workload uses less memory than the JVM overhead of a single idle Elasticsearch node.

This efficiency translates directly to compute cost savings. Where Elasticsearch requires large, memory-optimized instances (like AWS r6g.2xlarge at $0.40/hour per node), Parseable can run effectively on general-purpose instances a fraction of the size.

AI-Native Analysis

Parseable includes built-in AI-powered analysis capabilities that can surface anomalies, correlate events across telemetry types, and provide natural language interaction with your observability data. Elasticsearch requires third-party plugins or external ML pipelines to achieve similar functionality.

As AI-driven observability becomes the standard rather than the exception, having AI capabilities built into the core platform rather than bolted on as an afterthought provides a meaningful advantage in mean time to resolution (MTTR) and proactive incident detection.

Deployment Flexibility and Transparency

Elasticsearch changed its license from Apache 2.0 to SSPL (Server Side Public License) in 2021, a license that the Open Source Initiative does not recognize as open source. This led to the AWS fork (OpenSearch) and fragmented the ecosystem.

Parseable is available as Parseable Cloud — a managed service starting at $0.37/GB ingested ($29/month minimum) — and as a self-hosted deployment. The source code is available at github.com/parseablehq/parseable under AGPL-3.0, so you can inspect, audit, and build on it without licensing ambiguity.

Migration Guide: Elasticsearch to Parseable

Migrating from Elasticsearch to Parseable is a structured process. Here is a step-by-step guide for platform engineers.

Step 1: Inventory Your Elasticsearch Usage

Before migrating, document your current Elasticsearch deployment:

# Get cluster health and node count
curl -s localhost:9200/_cluster/health | jq '.status, .number_of_nodes, .number_of_data_nodes'
 
# Get index sizes and document counts
curl -s localhost:9200/_cat/indices?v&s=store.size:desc | head -20
 
# Get current ingestion rate
curl -s localhost:9200/_cluster/stats | jq '.indices.docs.count, .indices.store.size_in_bytes'

Document the following:

• Daily ingestion volume (GB/day)
• Number and sizes of indexes
• Retention period per index
• Critical Kibana dashboards and saved searches
• Alerting rules configured in ElastAlert or Kibana Alerts
• Data sources (Beats, Logstash pipelines, direct API ingestion)

Step 2: Deploy Parseable

The fastest path is Parseable Cloud — sign up for a free tier and skip to Step 3. To self-host, deploy in under five minutes using Docker:

docker run -p 8000:8000 \
  -e P_S3_URL=https://s3.amazonaws.com \
  -e P_S3_ACCESS_KEY=your-access-key \
  -e P_S3_SECRET_KEY=your-secret-key \
  -e P_S3_BUCKET=parseable-data \
  -e P_S3_REGION=us-east-1 \
  parseable/parseable:latest

For production deployments, use Kubernetes with the official Helm chart:

helm repo add parseable https://charts.parseable.com
helm install parseable parseable/parseable \
  --set storage.s3.bucket=parseable-data \
  --set storage.s3.region=us-east-1

Step 3: Redirect Telemetry Sources

Since most modern log pipelines use OpenTelemetry or support multiple outputs, you can run Parseable in parallel with Elasticsearch during migration.

If using OpenTelemetry Collector, add Parseable as an OTLP exporter:

exporters:
  otlphttp/parseable:
    endpoint: "http://parseable:8000/v1"
    headers:
      Authorization: "Basic <base64-encoded-credentials>"
 
service:
  pipelines:
    logs:
      receivers: [filelog, otlp]
      processors: [batch]
      exporters: [elasticsearch, otlphttp/parseable]

If using Fluentd or Fluent Bit, add an HTTP output pointing to Parseable's REST API:

[OUTPUT]
    Name          http
    Match         *
    Host          parseable-host
    Port          8000
    URI           /api/v1/logstream/your-stream
    Format        json
    Header        Authorization Basic <base64-encoded-credentials>

If using Logstash, add an HTTP output:

output {
  http {
    url => "http://parseable-host:8000/api/v1/logstream/your-stream"
    http_method => "post"
    format => "json"
    headers => {
      "Authorization" => "Basic <base64-encoded-credentials>"
    }
  }
}

Step 4: Translate Critical Queries

Map your most-used Elasticsearch queries to Parseable SQL:

Step 5: Rebuild Dashboards

Parseable includes a built-in web console for visualization and exploration. For teams that prefer Grafana, Parseable supports Grafana as a datasource, allowing you to recreate Kibana dashboards in Grafana with SQL-based panels.

Step 6: Validate and Cut Over

Run both systems in parallel for 1-2 weeks. Compare:

• Query results match between Elasticsearch and Parseable
• Alerting coverage is equivalent
• Dashboard visualizations show the same data
• Ingestion reliability (no data loss during pipeline switchover)

Once validated, remove the Elasticsearch exporter from your pipeline and decommission the Elasticsearch cluster.

Step 7: Decommission Elasticsearch

# Disable shard allocation to prevent rebalancing during shutdown
curl -X PUT localhost:9200/_cluster/settings -H 'Content-Type: application/json' -d '{
  "persistent": { "cluster.routing.allocation.enable": "none" }
}'
 
# Stop Elasticsearch nodes
sudo systemctl stop elasticsearch
 
# Archive any data you need for compliance
# Then remove Elasticsearch infrastructure

For detailed migration guidance, see our complete ELK to Parseable migration guide.

Frequently Asked Questions

Can Parseable handle the same query volume as Elasticsearch?

Yes. Parseable's query performance is optimized for analytical workloads on time-series data, which is the primary access pattern for log analytics. Parquet's columnar format with predicate pushdown and column pruning means Parseable reads only the data it needs. For time-bounded queries (which represent over 95% of log queries in practice), Parseable performs comparably or better than Elasticsearch because it scans less data. For full-text keyword search across unbounded time ranges, Elasticsearch's inverted indexes have an advantage, but this query pattern is rare in production log analytics.

Is Parseable production-ready for enterprise scale?

Parseable is used in production by organizations ingesting terabytes per day. The architecture is designed to scale horizontally, with multiple ingest nodes writing to the same object storage backend. For organizations that want a managed experience, Parseable Cloud provides a fully managed deployment starting at $0.37/GB ingested. For teams that prefer self-hosting, the source code is available at github.com/parseablehq/parseable.

What happens to my existing Elasticsearch data during migration?

Your existing Elasticsearch data remains accessible during the migration. We recommend running both systems in parallel, with your log pipeline dual-writing to Elasticsearch and Parseable. Once the Parseable retention period covers your required lookback window, you can decommission Elasticsearch. Historical data in Elasticsearch can be queried until the cluster is shut down, and critical historical data can be exported and re-ingested into Parseable if needed.

Does Parseable support full-text search like Elasticsearch?

Parseable supports full-text search through SQL LIKE and regex operators. While Elasticsearch's inverted indexes provide faster raw full-text search performance for specific keyword lookups, Parseable's columnar storage with compression means it scans significantly less data for each query. In practice, the query latency difference is minimal for most log analytics use cases. Parseable's SQL interface also provides analytical capabilities (aggregations, window functions, CTEs) that are cumbersome to express in Elasticsearch's Query DSL.

How does Parseable handle high availability?

Parseable achieves high availability through its architecture. The stateless binary can be deployed behind a load balancer with multiple instances writing to the same S3 bucket. If an instance fails, another instance picks up the work. S3 itself provides 99.999999999% (11 nines) durability and 99.99% availability. There is no cluster quorum to maintain, no split-brain scenario to worry about, and no shard reallocation to wait for. Recovery from a node failure takes seconds, not the minutes or hours that Elasticsearch shard reallocation requires.

Can I use Parseable for metrics and traces alongside logs?

Absolutely. Unlike Elasticsearch, which is a log storage backend that requires separate tools for metrics (Prometheus, Metricbeat) and traces (Elastic APM, Jaeger), Parseable is a unified MELT observability platform. It accepts logs, metrics, events, and traces through its native OTLP endpoint. All telemetry types are stored in the same S3 backend and queryable through the same SQL interface. This unification eliminates the tool sprawl that Elasticsearch deployments inevitably create.

The Best Elasticsearch Alternative for Log Analytics

Elasticsearch served the industry well when it was the best option available. In 2026, it is no longer the best option for log analytics. Its architecture imposes unnecessary operational complexity, storage costs, and scaling challenges that purpose-built observability platforms have eliminated.

Parseable offers a path forward that is not just incrementally better, but architecturally different. S3-native storage, Rust efficiency, SQL queries, unified MELT observability, and single-binary simplicity are not minor improvements. They represent a fundamental rethinking of how observability infrastructure should work.

If you are spending more time managing your Elasticsearch cluster than analyzing the data in it, it is time to evaluate Parseable.

For more comparisons and migration guides, explore our articles on Splunk alternatives, open source log management tools, and the complete ELK to Parseable migration guide.

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