Building a Full-Text Search App Using Docker and Elasticsearch | Hacker News Reader

Building a Full-Text Search App Using Docker and Elasticsearch

blog.patricktriest.com

98 pointsquotable_cow13 comments

The sync issues between an RDBMS and ElasticSearch are definitely the most annoying aspect of getting something like this working.

In the case of my current project, I've got an 'indexer' service whose sole job is to await messages from RabbitMQ to create/update/delete records to/from the index.

But sometimes an update may get missed for any number of reasons. So, I've got a systemd timer that fires off a little script to go through my audit tables in Postgres and make sure all changes since the last run were actually, in fact, synced to ElasticSearch.

And I'm still wondering if what I'm doing is 'good enough.' How else could this thing fail?

What if your server crashes after you commit to the database, but before you submit to elastic-search? Even if submitting is flawless, this still presents a problem.

Depends on which server :D The Database, ElasticSearch, RabbitMQ, and the application services are on different VMs (in my case, Linodes).

Committing to the database will trigger an entry (via a trigger function) in an audit table. So there is still a record of the transaction. After the system is restored to a healthy state, the next time the sync script is run (either automatically or manually if I'm feeling anxious), it should catch up.

Of course, in a worst-case scenario, I could always rebuild the index :O.

A server crash should be a rare event but if you reindex data regularly, it should not be a huge worry.

In Rails, the searchkick gem seems to do a good job of solving this pain point

This is a great, really comprehensive walk-through!

For folks that want to work with a bit more than books, there are a bunch of examples on Elastic's Github profile - I've found this useful for testing queries that pull back visualizations, etc: https://github.com/elastic/examples/

If you want to test query results without building a whole front-end / API, you can also use Sense / Kibana to query directly via the query language or, enable CORS and use something like POSTman.

If you don't want to use node I'd also recommend taking a look at their python client.

This is epic! The COO at Docker is one of my advisors and some customers of ours are doing really neat open source ElasticSearch integrations:

Even comes with a dashboard for dataviz and querying the FTS system: https://github.com/lmangani/gun-elastic/blob/master/README.M...

And one of the other comments asked about how to keep their RDBMS synced with ElasticSearch, that is also what the above linked person is working on, I know they already have it syncing with Cassandra. I'm sure Postgres is just a plug and play away.

When I run elastic search out of the box, I've noticed that search is quite slow, on the order of 5 seconds per search, over say 10000 PDF documents (papers), on a single node. Is this normal? And is there an obvious place to look or a (quick) fix which doesn't require me to delve into the ES details too much? (as I'm not a Java person)

Sounds slow, but it depends on the total size of the index. I could see 10K PDFs being big enough to slow things down. The easiest thing to try is setting the Java heap to half the RAM up to 32GB. The more RAM the better in general. I have also found setting the number of shards to the number of CPU cores to be optimal in many cases, however if a shard is too big (>50GB) then it can start causing other problems. Again the more CPU cores the better in general.

Here are the relevant docs, and don't worry you don't need to know Java to get good performance.

indexing: https://www.elastic.co/guide/en/elasticsearch/reference/curr...

searching: https://www.elastic.co/guide/en/elasticsearch/reference/curr...

disk usage: https://www.elastic.co/guide/en/elasticsearch/reference/curr...

Thanks! I'll have a look at that!

This is a great overview. Some numbers from step 4 - size of data set, time to load index and space used on disk by that index+ES would be interesting to see. I have a feeling for small datasets postgres/mongo even sqlites FTS would perform better.

Great tutorial

It's really nice to see a full-stack example like this that builds out a useful application while demonstrating how to utilise a broad spectrum of technologies. Good job.

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