Before you begin reading this guide, we recommend you run Elasticsearch Error Check-Up which can resolve issues that cause many errors.
This guide will help you check for common problems that cause the log ” No river _meta document found after attempts ” to appear. It’s important to understand the issues related to the log, so to get started, read the general overview on common issues and tips related to the Elasticsearch concepts: document and routing.
Advanced users might want to skip right to the common problems section in each concept or try running the Check-Up which analyses ES to pinpoint the cause of many errors and provides suitable actionable recommendations how to resolve them (free tool that requires no installation).
Document in Elasticsearch
What is an Elasticsearch document?
While an SQL database has rows of data stored in tables, Elasticsearch stores data as multiple documents inside an index. This is where the analogy must end however, since the way that Elasticsearch treats documents and indices differs significantly from a relational database.
For example, documents could be:
- Products in an e-commerce index
- Log lines in a data logging application
- Invoice lines in an invoicing system
Document fields
Each document is essentially a JSON structure, which is ultimately considered to be a series of key:value pairs. These pairs are then indexed in a way that is determined by the document mapping. The mapping defines the field data type as text, keyword, float, time, geo point or various other data types.
Elasticsearch documents are described as schema-less because Elasticsearch does not require us to pre-define the index field structure, nor does it require all documents in an index to have the same structure. However, once a field is mapped to a given data type, then all documents in the index must maintain that same mapping type.
Each field can also be mapped in more than one way in the index. This can be useful because we may want a keyword structure for aggregations, and at the same time be able to keep an analysed data structure which enables us to carry out full text searches for individual words in the field.
For a full discussion on mapping please see here.
Document source
An Elasticsearch document _source consists of the original JSON source data before it is indexed. This data is retrieved when fetched by a search query.
Document metadata
Each document is also associated with metadata, the most important items being:
_index – The index where the document is stored
_id – The unique ID which identifies the document in the index
Documents and index architecture
Note that different applications could consider a “document” to be a different thing. For example, in an invoicing system, we could have an architecture which stores invoices as documents (1 document per invoice), or we could have an index structure which stores multiple documents as “invoice lines” for each invoice. The choice would depend on how we want to store, map and query the data.
Examples:
Creating a document in the user’s index:
POST /users/_doc { "name" : "Petey", "lastname" : "Cruiser", "email" : "petey@gmail.com" }
In the above request, we haven’t mentioned an ID for the document so the index operation generates a unique ID for the document. Here _doc is the type of document.
POST /users/_doc/1 { "name" : "Petey", "lastname" : "Cruiser", "email" : "petey@gmail.com" }
In the above query, the document will be created with ID 1.
You can use the below ‘GET’ query to get a document from the index using ID:
GET /users/_doc/1
Below is the result, which contains the document (in _source field) as metadata:
{ "_index": "users", "_type": "_doc", "_id": "1", "_version": 1, "_seq_no": 1, "_primary_term": 1, "found": true, "_source": { "name": "Petey", "lastname": "Cruiser", "email": "petey@gmail.com" } }
Notes
Starting version 7.0 types are deprecated, so for backward compatibility on version 7.x all docs are under type ‘_doc’, starting 8.x type will be completely removed from ES APIs.
Overview
In Elasticsearch, routing refers to document routing. When you index a document, Elasticsearch will determine which shard will be used to index the document to.
The shard is selected based on the following formula:
shard = hash(_routing) % number_of_primary_shards
Where the default value of _routing is _id.
It is important to know which shard the document is routed to, because Elasticsearch will need to determine where to find that document later on for document retrieval requests.
Examples
In twitter index with 2 primary shards, the document with _id equal to “440” gets routed to the shard number:
shard = hash( 440 ) % 2 PUT twitter/_doc/440 { ... }
Notes and good things to know
- In order to improve search performance speed you can create custom routing. For example, you can enable custom routing that will ensure only a single shard is queried (the shard that contains your data).
- To create custom routing in Elasticsearch, you will need to configure and define that not all routing will be completed by default settings. ( v <= 5.0)
PUT my_index/customer/_mapping { "order":{ "_routing":{ "required":true } } }
- This will ensure that every document in the “customer” type must specify a custom routing. For elasticsearch 6 or above you will need to update the same mapping as:
PUT my_index/_mapping { "order":{ "_routing":{ "required":true } } }
Log Context
Log “no river _meta document found after {} attempts” classname is RiversRouter.java
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
// At least one type does not have _meta; so we are // going to reschedule some checks if (!metaFound) { if (countDown.countDown()) { logger.warn("no river _meta document found after {} attempts"; RIVER_START_MAX_RETRIES); } else { logger.debug("no river _meta document found retrying in {} ms"; RIVER_START_RETRY_INTERVAL.millis()); try { threadPool.schedule(RIVER_START_RETRY_INTERVAL; ThreadPool.Names.GENERIC; new Runnable() { Override
Run the Check-Up to get customized recommendations like this:

Heavy merges detected in specific nodes

Description
A large number of small shards can slow down searches and cause cluster instability. Some indices have shards that are too small…

Recommendations Based on your specific ES deployment you should…
Based on your specific ES deployment you should…
X-PUT curl -H [a customized code snippet to resolve the issue]