Cloudera Search Frequently Asked Questions
This section includes answers to questions commonly asked about Search for CDH. Questions are divided into the following categories:
The following are general questions about Cloudera Search and the answers to those questions.
What is Cloudera Search?
Cloudera Search is Apache Solr integrated with CDH, including Apache Lucene, Apache SolrCloud, Apache Flume, Apache Tika, and Apache Hadoop MapReduce and HDFS. Cloudera Search also includes valuable integrations that make searching more scalable, easy to use, and optimized for both near-real-time and batch-oriented indexing. These integrations include Cloudera Morphlines, a customizable transformation chain that simplifies loading any type of data into Cloudera Search.
What is the difference between Lucene and Solr?
Lucene is a low-level search library that is accessed by a Java API. Solr is a search server that runs in a servlet container and provides structure and convenience around the underlying Lucene library.
What is Apache Tika?
The Apache Tika toolkit detects and extracts metadata and structured text content from various documents using existing parser libraries. Using the solrCell morphline command, the output from Apache Tika can be mapped to a Solr schema and indexed.
How does Cloudera Search relate to web search?
Traditional web search engines crawl web pages on the Internet for content to index. Cloudera Search indexes files and data that are stored in HDFS and HBase. To make web data available through Cloudera Search, it needs to be downloaded and stored in Cloudera's Distribution, including Apache Hadoop (CDH).
How does Cloudera Search relate to enterprise search?
Enterprise search connects with different backends (such as RDBMS and filesystems) and indexes data in all those systems. Cloudera Search is intended as a full-text search capability for data in CDH. Cloudera Search is a tool added to Cloudera's data processing platform and does not aim to be a stand-alone search solution, but rather a user-friendly interface to explore data in Hadoop and HBase.
How does Cloudera Search relate to custom search applications?
Custom and specialized search applications are an excellent complement to Cloudera's data-processing platform. Cloudera Search is not designed to be a custom application for niche vertical markets. However, Cloudera Search does include a simple search GUI through a plug-in application for Hue. It is based on the Solr API and allows for easy exploration, along with all of the other Hadoop frontend applications in Hue.
Do Search security features use Kerberos?
Yes, Cloudera Search includes support for Kerberos authentication. Search continues to use simple authentication with the anonymous user as the default configuration, but Search now supports changing the authentication scheme to Kerberos. All required packages are installed during the installation or upgrade process. Additional configuration is required before Kerberos is available in your environment.
Do I need to configure Sentry restrictions for each access mode, such as for the admin console and for the command line?
Sentry restrictions are consistently applied regardless of the way users attempt to complete actions. For example, restricting access to data in a collection consistently restricts that access, whether queries come from the command line, from a browser, or through the admin console.
Does Search support indexing data stored in JSON files and objects?
Yes, you can use the readJson and extractJsonPaths morphline commands that are included with the CDK to access JSON data and files. For more information, see cdk-morphlines-json.
Why do I get an error “no field name specified in query and no default specified via 'df' param" when I query a Schemaless collection?
Schemaless collections initially have no default or df setting. As a result, simple searches that might succeed on non-Schemaless collections may fail on Schemaless collections.
When a user submits a search, it must be clear which field Cloudera Search should query. A default field, or df, is often specified in solrconfig.xml, and when this is the case, users can submit queries that do not specify fields. In such situations, Solr uses the df value.
- Queries can specify any valid field name on which to search. In such a case, no df is required.
- Queries can specify a default field using the df parameter. In such a case, the df is specified in the query.
- You can uncomment the df section of the generated schemaless solrconfig.xml file and set the df parameter to the desired field. In such a case, all subsequent queries can use the df field in solrconfig.xml if no field or df value is specified.
Performance and Fail Over
The following are questions about performance and fail over in Cloudera Search and the answers to those questions.
How large of an index does Cloudera Search support per search server?
There are too many variables to provide a single answer to this question. Typically, a server can host from 10 to 300 million documents, with the underlying index as large as hundreds of gigabytes. To determine a reasonable maximum document quantity and index size for servers in your deployment, prototype with realistic data and queries.
What is the response time latency I can expect?
Many factors affect how quickly responses are returned. Some factors that contribute to latency include whether the system is also completing indexing, the type of fields you are searching, whether the search results require aggregation, and whether there are sufficient resources for your search services.
With appropriately-sized hardware, if the query results are found in memory, they may be returned within milliseconds. Conversely, complex queries requiring results aggregation over huge indexes may take a few seconds.
The time between when Search begins to work on indexing new data and when that data can be queried can be as short as a few seconds, depending on your configuration.
This high performance is supported by custom caching optimizations that Cloudera has added to the Solr/HDFS integration. These optimizations allow for rapid read and writes of files in HDFS, performing at or above the speeds of stand-alone Solr reading and writing from local disk.
What happens when a write to the Lucene indexer fails?
Cloudera Search provides two configurable, highly available, and fault-tolerant data ingestion schemes: near real-time ingestion using the Flume Solr Sink and MapReduce-based batch ingestion using the MapReduceIndexerTool. These approaches are discussed in more detail in Search High Availability.
What hardware or configuration changes can I make to improve Search performance?
Search performance can be constrained by CPU limits. If you're seeing bottlenecks, consider allocating more CPU to Search.
Are there settings that can help avoid out of memory (OOM) errors during data ingestion?
A data ingestion pipeline can be made up of many connected segments, each of which may need to be evaluated for sufficient resources. A common limiting factor is the relatively small default amount of permgen memory allocated to the flume JVM. Allocating additional memory to the Flume JVM may help avoid OOM errors. For example, for JVM settings for Flume, the following settings are often sufficient:
The following are questions about schema management in Cloudera Search and the answers to those questions.
If my schema changes, will I need to re-index all of my data and files?
When you change the schema, Cloudera recommends re-indexing. For example, if you add a new field to the index, apply the new field to all index entries through re-indexing. Re-indexing is required in such a case because existing documents do yet not have the field. Cloudera Search includes a MapReduce batch-indexing solution for re-indexing and a GoLive feature that assures updated indexes are dynamically served.
Note that, you should typically re-index after adding a new field, this is not necessary if the new field applies only to new documents or data. This is because, were indexing to be completed, existing documents would still have no data for the field, making the effort unnecessary.
For schema changes that only apply to queries, re-indexing is not necessary.
Can I extract fields based on regular expressions or rules?
Cloudera Search supports limited regular expressions in Search queries. For details, see Lucene Regular Expressions.
On data ingestion, Cloudera Search supports easy and powerful extraction of fields based on regular expressions. For example the grok morphline command supports field extraction using regular expressions.
Cloudera Search also includes support for rule directed ETL with an extensible rule engine, in the form of the tryRules morphline command.
Can I use nested schemas?
Cloudera Search does not support nesting documents in this release. Cloudera Search assumes documents in the Cloudera Search repository have a flat structure.
What is Apache Avro and how can I use an Avro schema for more flexible schema evolution?
The following are questions about supportability in Cloudera Search and the answers to those questions.
Does Cloudera Search support multiple languages?
Cloudera Search supports approximately 30 languages, including most Western European languages, as well as Chinese, Japanese, and Korean.
Which file formats does Cloudera Search support for indexing? Does it support searching images?
Cloudera Search uses the Apache Tika library for indexing many standard document formats. In addition, Cloudera Search supports indexing and searching Avro files and a wide variety of other file types such as log files, Hadoop Sequence Files, and CSV files. You can add support for indexing custom file formats using a morphline command plug-in.
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