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https://issues.apache.org/jira/browse/SOLR-5758?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13915182#comment-13915182
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Mark Miller commented on SOLR-5758:
-----------------------------------
An updated dump:
{noformat}
usage: hadoop [GenericOptions]... jar solr-map-reduce-*.jar
[--help] --output-dir HDFS_URI [--input-list URI]
--morphline-file FILE [--morphline-id STRING]
[--update-conflict-resolver FQCN] [--mappers INTEGER]
[--reducers INTEGER] [--max-segments INTEGER]
[--fair-scheduler-pool STRING] [--dry-run] [--log4j FILE]
[--verbose] [--show-non-solr-cloud] [--zk-host STRING] [--go-live]
[--collection STRING] [--go-live-threads INTEGER]
[HDFS_URI [HDFS_URI ...]]
MapReduce batch job driver that takes a morphline and creates a set of
Solr index shards from a set of input files and writes the indexes into
HDFS, in a flexible, scalable and fault-tolerant manner. It also supports
merging the output shards into a set of live customer facing Solr servers,
typically a SolrCloud. The program proceeds in several consecutive
MapReduce based phases, as follows:
1) Randomization phase: This (parallel) phase randomizes the list of input
files in order to spread indexing load more evenly among the mappers of
the subsequent phase.
2) Mapper phase: This (parallel) phase takes the input files, extracts the
relevant content, transforms it and hands SolrInputDocuments to a set of
reducers. The ETL functionality is flexible and customizable using chains
of arbitrary morphline commands that pipe records from one transformation
command to another. Commands to parse and transform a set of standard data
formats such as Avro, CSV, Text, HTML, XML, PDF, Word, Excel, etc. are
provided out of the box, and additional custom commands and parsers for
additional file or data formats can be added as morphline plugins. This is
done by implementing a simple Java interface that consumes a record (e.g.
a file in the form of an InputStream plus some headers plus contextual
metadata) and generates as output zero or more records. Any kind of data
format can be indexed and any Solr documents for any kind of Solr schema
can be generated, and any custom ETL logic can be registered and executed.
Record fields, including MIME types, can also explicitly be passed by
force from the CLI to the morphline, for example: hadoop ... -D
morphlineField._attachment_mimetype=text/csv
3) Reducer phase: This (parallel) phase loads the mapper's
SolrInputDocuments into one EmbeddedSolrServer per reducer. Each such
reducer and Solr server can be seen as a (micro) shard. The Solr servers
store their data in HDFS.
4) Mapper-only merge phase: This (parallel) phase merges the set of
reducer shards into the number of solr shards expected by the user, using
a mapper-only job. This phase is omitted if the number of shards is
already equal to the number of shards expected by the user.
5) Go-live phase: This optional (parallel) phase merges the output shards
of the previous phase into a set of live customer facing Solr servers,
typically a SolrCloud. If this phase is omitted you can explicitly point
each Solr server to one of the HDFS output shard directories.
Fault Tolerance: Mapper and reducer task attempts are retried on failure
per the standard MapReduce semantics. On program startup all data in the --
output-dir is deleted if that output directory already exists. If the
whole job fails you can retry simply by rerunning the program again using
the same arguments.
positional arguments:
HDFS_URI HDFS URI of file or directory tree to index.
(default: [])
optional arguments:
--help, -help, -h Show this help message and exit
--input-list URI Local URI or HDFS URI of a UTF-8 encoded file
containing a list of HDFS URIs to index, one URI
per line in the file. If '-' is specified, URIs
are read from the standard input. Multiple --
input-list arguments can be specified.
--morphline-id STRING The identifier of the morphline that shall be
executed within the morphline config file
specified by --morphline-file. If the --morphline-
id option is ommitted the first (i.e. top-most)
morphline within the config file is used.
Example: morphline1
--update-conflict-resolver FQCN
Fully qualified class name of a Java class that
implements the UpdateConflictResolver interface.
This enables deduplication and ordering of a
series of document updates for the same unique
document key. For example, a MapReduce batch job
might index multiple files in the same job where
some of the files contain old and new versions of
the very same document, using the same unique
document key.
Typically, implementations of this interface
forbid collisions by throwing an exception, or
ignore all but the most recent document version,
or, in the general case, order colliding updates
ascending from least recent to most recent
(partial) update. The caller of this interface (i.
e. the Hadoop Reducer) will then apply the
updates to Solr in the order returned by the
orderUpdates() method.
The default
RetainMostRecentUpdateConflictResolver
implementation ignores all but the most recent
document version, based on a configurable numeric
Solr field, which defaults to the
file_last_modified timestamp (default: org.apache.
solr.hadoop.dedup.
RetainMostRecentUpdateConflictResolver)
--mappers INTEGER Tuning knob that indicates the maximum number of
MR mapper tasks to use. -1 indicates use all map
slots available on the cluster. (default: -1)
--reducers INTEGER Tuning knob that indicates the number of reducers
to index into. -1 indicates use all reduce slots
available on the cluster. 0 indicates use one
reducer per output shard, which disables the
mtree merge MR algorithm. The mtree merge MR
algorithm improves scalability by spreading load
(in particular CPU load) among a number of
parallel reducers that can be much larger than
the number of solr shards expected by the user.
It can be seen as an extension of concurrent
lucene merges and tiered lucene merges to the
clustered case. The subsequent mapper-only phase
merges the output of said large number of
reducers to the number of shards expected by the
user, again by utilizing more available
parallelism on the cluster. (default: -1)
--max-segments INTEGER
Tuning knob that indicates the maximum number of
segments to be contained on output in the index
of each reducer shard. After a reducer has built
its output index it applies a merge policy to
merge segments until there are <= maxSegments
lucene segments left in this index. Merging
segments involves reading and rewriting all data
in all these segment files, potentially multiple
times, which is very I/O intensive and time
consuming. However, an index with fewer segments
can later be merged faster, and it can later be
queried faster once deployed to a live Solr
serving shard. Set maxSegments to 1 to optimize
the index for low query latency. In a nutshell, a
small maxSegments value trades indexing latency
for subsequently improved query latency. This can
be a reasonable trade-off for batch indexing
systems. (default: 1)
--fair-scheduler-pool STRING
Optional tuning knob that indicates the name of
the fair scheduler pool to submit jobs to. The
Fair Scheduler is a pluggable MapReduce scheduler
that provides a way to share large clusters. Fair
scheduling is a method of assigning resources to
jobs such that all jobs get, on average, an equal
share of resources over time. When there is a
single job running, that job uses the entire
cluster. When other jobs are submitted, tasks
slots that free up are assigned to the new jobs,
so that each job gets roughly the same amount of
CPU time. Unlike the default Hadoop scheduler,
which forms a queue of jobs, this lets short jobs
finish in reasonable time while not starving long
jobs. It is also an easy way to share a cluster
between multiple of users. Fair sharing can also
work with job priorities - the priorities are
used as weights to determine the fraction of
total compute time that each job gets.
--dry-run Run in local mode and print documents to stdout
instead of loading them into Solr. This executes
the morphline in the client process (without
submitting a job to MR) for quicker turnaround
during early trial & debug sessions. (default:
false)
--log4j FILE Relative or absolute path to a log4j.properties
config file on the local file system. This file
will be uploaded to each MR task. Example:
/path/to/log4j.properties
--verbose, -v Turn on verbose output. (default: false)
--show-non-solr-cloud Also show options for Non-SolrCloud mode as part
of --help. (default: false)
Required arguments:
--output-dir HDFS_URI HDFS directory to write Solr indexes to. Inside
there one output directory per shard will be
generated. Example: hdfs://c2202.mycompany.
com/user/$USER/test
--morphline-file FILE Relative or absolute path to a local config file
that contains one or more morphlines. The file
must be UTF-8 encoded. Example:
/path/to/morphline.conf
Cluster arguments:
Arguments that provide information about your Solr cluster.
--zk-host STRING The address of a ZooKeeper ensemble being used by
a SolrCloud cluster. This ZooKeeper ensemble will
be examined to determine the number of output
shards to create as well as the Solr URLs to
merge the output shards into when using the --go-
live option. Requires that you also pass the --
collection to merge the shards into.
The --zk-host option implements the same
partitioning semantics as the standard SolrCloud
Near-Real-Time (NRT) API. This enables to mix
batch updates from MapReduce ingestion with
updates from standard Solr NRT ingestion on the
same SolrCloud cluster, using identical unique
document keys.
Format is: a list of comma separated host:port
pairs, each corresponding to a zk server.
Example: '127.0.0.1:2181,127.0.0.1:2182,127.0.0.1:
2183' If the optional chroot suffix is used the
example would look like: '127.0.0.1:2181/solr,
127.0.0.1:2182/solr,127.0.0.1:2183/solr' where
the client would be rooted at '/solr' and all
paths would be relative to this root - i.e.
getting/setting/etc... '/foo/bar' would result in
operations being run on '/solr/foo/bar' (from the
server perspective).
Go live arguments:
Arguments for merging the shards that are built into a live Solr
cluster. Also see the Cluster arguments.
--go-live Allows you to optionally merge the final index
shards into a live Solr cluster after they are
built. You can pass the ZooKeeper address with --
zk-host and the relevant cluster information will
be auto detected. (default: false)
--collection STRING The SolrCloud collection to merge shards into
when using --go-live and --zk-host. Example:
collection1
--go-live-threads INTEGER
Tuning knob that indicates the maximum number of
live merges to run in parallel at one time.
(default: 1000)
Generic options supported are
--conf <configuration file>
specify an application configuration file
-D <property=value> use value for given property
--fs <local|namenode:port>
specify a namenode
--jt <local|jobtracker:port>
specify a job tracker
--files <comma separated list of files>
specify comma separated files to be copied to the
map reduce cluster
--libjars <comma separated list of jars>
specify comma separated jar files to include in
the classpath.
--archives <comma separated list of archives>
specify comma separated archives to be unarchived
on the compute machines.
The general command line syntax is
bin/hadoop command [genericOptions] [commandOptions]
Examples:
# (Re)index an Avro based Twitter tweet file:
sudo -u hdfs hadoop \
--config /etc/hadoop/conf.cloudera.mapreduce1 \
jar target/solr-map-reduce-*.jar \
-D 'mapred.child.java.opts=-Xmx500m' \
--log4j src/test/resources/log4j.properties \
--morphline-file
../search-core/src/test/resources/test-morphlines/tutorialReadAvroContainer.conf
\
--solr-home-dir src/test/resources/solr/minimr \
--output-dir hdfs://c2202.mycompany.com/user/$USER/test \
--shards 1 \
hdfs:///user/$USER/test-documents/sample-statuses-20120906-141433.avro
# Go live by merging resulting index shards into a live Solr cluster
# (explicitly specify Solr URLs - for a SolrCloud cluster see next example):
sudo -u hdfs hadoop \
--config /etc/hadoop/conf.cloudera.mapreduce1 \
jar target/solr-map-reduce-*.jar \
-D 'mapred.child.java.opts=-Xmx500m' \
--log4j src/test/resources/log4j.properties \
--morphline-file
../search-core/src/test/resources/test-morphlines/tutorialReadAvroContainer.conf
\
--solr-home-dir src/test/resources/solr/minimr \
--output-dir hdfs://c2202.mycompany.com/user/$USER/test \
--shard-url http://solr001.mycompany.com:8983/solr/collection1 \
--shard-url http://solr002.mycompany.com:8983/solr/collection1 \
--go-live \
hdfs:///user/foo/indir
# Go live by merging resulting index shards into a live SolrCloud cluster
# (discover shards and Solr URLs through ZooKeeper):
sudo -u hdfs hadoop \
--config /etc/hadoop/conf.cloudera.mapreduce1 \
jar target/solr-map-reduce-*.jar \
-D 'mapred.child.java.opts=-Xmx500m' \
--log4j src/test/resources/log4j.properties \
--morphline-file
../search-core/src/test/resources/test-morphlines/tutorialReadAvroContainer.conf
\
--output-dir hdfs://c2202.mycompany.com/user/$USER/test \
--zk-host zk01.mycompany.com:2181/solr \
--collection collection1 \
--go-live \
hdfs:///user/foo/indir
{noformat}
> need ref guide doc on building indexes with mapreduce (morphlines-cell
> contrib)
> -------------------------------------------------------------------------------
>
> Key: SOLR-5758
> URL: https://issues.apache.org/jira/browse/SOLR-5758
> Project: Solr
> Issue Type: Task
> Components: documentation
> Reporter: Hoss Man
> Assignee: Mark Miller
> Fix For: 4.8
>
>
> This is marked experimental for 4.7, but we should have a section on it in
> the ref guide in 4.8
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